From 8f06185f5f916074da845332013238c80941ab7e Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Thu, 11 Jan 2024 11:01:40 +0100
Subject: [PATCH 01/43] WIP: add benchmark setup for Ridge

---
 ..._benchmark_results_file_sanity_checks.yaml |   1 +
 .../sync_benchmark_files_to_gsheet.yaml       |   3 +
 .github/workflows/test_cpu_benchmarks.yaml    |   2 +
 README.md                                     |   1 +
 benchmarks/kmeans/objective.py                |  11 +-
 benchmarks/ridge/consolidate_result_csv.py    | 647 ++++++++++++++++++
 benchmarks/ridge/datasets/simulated_blobs.py  |  41 ++
 benchmarks/ridge/objective.py                 |  86 +++
 benchmarks/ridge/solvers/scikit_learn.py      |  76 ++
 9 files changed, 863 insertions(+), 5 deletions(-)
 create mode 100644 benchmarks/ridge/consolidate_result_csv.py
 create mode 100644 benchmarks/ridge/datasets/simulated_blobs.py
 create mode 100644 benchmarks/ridge/objective.py
 create mode 100644 benchmarks/ridge/solvers/scikit_learn.py

diff --git a/.github/workflows/run_benchmark_results_file_sanity_checks.yaml b/.github/workflows/run_benchmark_results_file_sanity_checks.yaml
index d427868..a93e386 100644
--- a/.github/workflows/run_benchmark_results_file_sanity_checks.yaml
+++ b/.github/workflows/run_benchmark_results_file_sanity_checks.yaml
@@ -19,3 +19,4 @@ jobs:
       run: |
         python ./benchmarks/kmeans/consolidate_result_csv.py ./benchmarks/kmeans/results.csv --check-csv
         python ./benchmarks/pca/consolidate_result_csv.py ./benchmarks/pca/results.csv --check-csv
+        python ./benchmarks/ridge/consolidate_result_csv.py ./benchmarks/ridge/results.csv --check-csv
diff --git a/.github/workflows/sync_benchmark_files_to_gsheet.yaml b/.github/workflows/sync_benchmark_files_to_gsheet.yaml
index cbd2f25..24505a6 100644
--- a/.github/workflows/sync_benchmark_files_to_gsheet.yaml
+++ b/.github/workflows/sync_benchmark_files_to_gsheet.yaml
@@ -24,8 +24,11 @@ jobs:
       run: |
         python ./benchmarks/kmeans/consolidate_result_csv.py ./benchmarks/kmeans/results.csv --check-csv
         python ./benchmarks/pca/consolidate_result_csv.py ./benchmarks/pca/results.csv --check-csv
+        python ./benchmarks/ridge/consolidate_result_csv.py ./benchmarks/pca/results.csv --check-csv
         echo "$GSPREAD_SERVICE_ACCOUNT_AUTH_KEY" > service_account.json
         python ./benchmarks/kmeans/consolidate_result_csv.py ./benchmarks/kmeans/results.csv \
           --sync-to-gspread --gspread-url $GSPREAD_URL --gspread-auth-key ./service_account.json
         python ./benchmarks/pca/consolidate_result_csv.py ./benchmarks/pca/results.csv \
           --sync-to-gspread --gspread-url $GSPREAD_URL --gspread-auth-key ./service_account.json
+        python ./benchmarks/ridge/consolidate_result_csv.py ./benchmarks/pca/results.csv \
+          --sync-to-gspread --gspread-url $GSPREAD_URL --gspread-auth-key ./service_account.json
diff --git a/.github/workflows/test_cpu_benchmarks.yaml b/.github/workflows/test_cpu_benchmarks.yaml
index 57b0707..31cfda8 100644
--- a/.github/workflows/test_cpu_benchmarks.yaml
+++ b/.github/workflows/test_cpu_benchmarks.yaml
@@ -143,3 +143,5 @@ jobs:
         PYTHONPATH=$PYTHONPATH:$(realpath ../../kmeans_dpcpp/) benchopt run --no-plot -l -d Simulated_correlated_data[n_samples=1000,n_features=14]
         cd ../pca
         benchopt run --no-plot -l -d Simulated_correlated_data[n_samples=100,n_features=100]
+        cd ../ridge
+        benchopt run --no-plot -l -d Simulated_correlated_data[n_samples=100,n_features=100]  # TODO add relevant parameters
diff --git a/README.md b/README.md
index 82f5e88..214f473 100644
--- a/README.md
+++ b/README.md
@@ -18,6 +18,7 @@ hardware.
 Benchmarks are currently available for the following algorithms:
 - [k-means](https://github.com/soda-inria/sklearn-engine-benchmarks/tree/main/benchmarks/kmeans)
 - [PCA](https://github.com/soda-inria/sklearn-engine-benchmarks/tree/main/benchmarks/pca)
+- [Ridge](https://github.com/soda-inria/sklearn-engine-benchmarks/tree/main/benchmarks/pca)
 
 Here is a (non-exhaustive) list of libraries that are compared in the benchmarks:
 - [scikit-learn](https://scikit-learn.org/stable/index.html)
diff --git a/benchmarks/kmeans/objective.py b/benchmarks/kmeans/objective.py
index 63f3bf1..c3e860c 100644
--- a/benchmarks/kmeans/objective.py
+++ b/benchmarks/kmeans/objective.py
@@ -32,15 +32,15 @@ def set_data(self, X, **dataset_parameters):
         self.X = X
         dtype = X.dtype
 
-        if self.init == "random" or self.sample_weight == "random":
-            rng = np.random.default_rng(self.random_state)
-
         if self.sample_weight == "None":
             sample_weight = None
         elif self.sample_weight == "unary":
             sample_weight = np.ones(len(X), dtype=dtype)
         elif self.sample_weight == "random":
-            sample_weight = rng.random(size=len(X)).astype(dtype)
+            rng_sample_weight = np.random.default_rng(
+                dataset_parameters["sample_weight"] + 1
+            )
+            sample_weight = rng_sample_weight.random(size=len(X)).astype(dtype)
         else:
             raise ValueError(
                 "Expected 'sample_weight' parameter to be either equal to 'None', "
@@ -48,8 +48,9 @@ def set_data(self, X, **dataset_parameters):
             )
 
         if self.init == "random":
+            rng_init = np.random.default_rng(self.random_state)
             init = np.array(
-                rng.choice(X, self.n_clusters, replace=False), dtype=X.dtype
+                rng_init.choice(X, self.n_clusters, replace=False), dtype=X.dtype
             )
         elif self.init == "k-means++":
             init = self.init
diff --git a/benchmarks/ridge/consolidate_result_csv.py b/benchmarks/ridge/consolidate_result_csv.py
new file mode 100644
index 0000000..3f780e9
--- /dev/null
+++ b/benchmarks/ridge/consolidate_result_csv.py
@@ -0,0 +1,647 @@
+import hashlib
+from functools import partial
+from io import BytesIO
+from itertools import zip_longest
+from operator import attrgetter
+
+import numpy as np
+import pandas as pd
+from pandas.io.parsers.readers import STR_NA_VALUES
+
+GOOGLE_WORKSHEET_NAME = "Ridge"
+
+DATES_FORMAT = "%Y-%m-%d"
+
+BENCHMARK_DEFINING_COLUMNS = [
+    "objective_objective_param___name",
+    "objective_dataset_param___name",
+    "objective_dataset_param_n_samples",
+    "objective_dataset_param_n_features",
+    "objective_dataset_param_n_targets",
+    "objective_dataset_param_dtype",
+    "objective_dataset_param_random_state",
+    "objective_objective_param_alpha",
+    "objective_objective_param_fit_intercept",
+    "objective_objective_param_max_iter",
+    "objective_objective_param_tol",
+    "objective_objective_param_sample_weight",
+    "objective_objective_param_random_state",
+]
+
+BENCHMARK_DEFINING_COLUMNS = sorted(BENCHMARK_DEFINING_COLUMNS)
+_benchmark_defining_columns_identifier = "".join(sorted(BENCHMARK_DEFINING_COLUMNS))
+
+BACKEND_PROVIDER = "Backend provider"
+COMMENT = "Comment"
+COMPUTE_DEVICE = "Compute device"
+COMPUTE_RUNTIME = "Compute runtime"
+DATA_RANDOM_STATE = "Data random state"
+DATA_SAMPLE_WEIGHTS = "Data sample weights"
+DTYPE = "Dtype"
+ALPHA = "alpha"
+NB_DATA_FEATURES = "Nb data features"
+NB_DATA_SAMPLES = "Nb data samples"
+NB_DATA_TARGETS = "Nb data targets"
+SOLVER = "Solver"
+PLATFORM = "Platform"
+PLATFORM_ARCHITECTURE = "Platform architecture"
+PLATFORM_RELEASE = "Platform release"
+SYSTEM_CPUS = "Nb cpus"
+SYSTEM_PROCESSOR = "Cpu name"
+SYSTEM_RAM = "RAM (GB)"
+SYSTEM_GPU = "Gpu name"
+RESULT_NB_ITERATIONS = "Result nb iterations"
+OBJECTIVE_FUNCTION_VALUE = "Result objective value"
+VERSION_INFO = "Version info"
+RUN_DATE = "Run date"
+SOLVER_RANDOM_STATE = "Solver random state"
+WALLTIME = "Walltime"
+
+BENCHMARK_ID_NAME = "Benchmark id"
+
+TABLE_DISPLAY_ORDER = [
+    BENCHMARK_ID_NAME,
+    DTYPE,
+    NB_DATA_SAMPLES,
+    NB_DATA_FEATURES,
+    NB_DATA_TARGETS,
+    DATA_SAMPLE_WEIGHTS,
+    ALPHA,
+    WALLTIME,
+    BACKEND_PROVIDER,
+    COMPUTE_DEVICE,
+    COMPUTE_RUNTIME,
+    SOLVER,
+    SYSTEM_CPUS,
+    SYSTEM_PROCESSOR,
+    SYSTEM_GPU,
+    SYSTEM_RAM,
+    PLATFORM,
+    PLATFORM_ARCHITECTURE,
+    PLATFORM_RELEASE,
+    RUN_DATE,
+    VERSION_INFO,
+    COMMENT,
+    RESULT_NB_ITERATIONS,
+    OBJECTIVE_FUNCTION_VALUE,
+    DATA_RANDOM_STATE,
+    SOLVER_RANDOM_STATE,
+]
+
+COLUMNS_DTYPES = {
+    BENCHMARK_ID_NAME: str,
+    DTYPE: str,
+    NB_DATA_SAMPLES: np.int64,
+    NB_DATA_FEATURES: np.int64,
+    NB_DATA_TARGETS: np.int64,
+    ALPHA: np.float64,
+    WALLTIME: np.float64,
+    BACKEND_PROVIDER: str,
+    COMPUTE_DEVICE: str,
+    COMPUTE_RUNTIME: str,
+    RESULT_NB_ITERATIONS: np.int64,
+    OBJECTIVE_FUNCTION_VALUE: np.float64,
+    SOLVER: str,
+    PLATFORM: str,
+    PLATFORM_ARCHITECTURE: str,
+    PLATFORM_RELEASE: str,
+    SYSTEM_CPUS: np.int64,
+    SYSTEM_PROCESSOR: str,
+    SYSTEM_GPU: str,
+    SYSTEM_RAM: np.int64,
+    DATA_RANDOM_STATE: np.int64,
+    SOLVER_RANDOM_STATE: np.int64,
+    VERSION_INFO: str,
+    RUN_DATE: str,
+    COMMENT: str,
+}
+
+COLUMNS_WITH_NONE_STRING = [DATA_SAMPLE_WEIGHTS]
+
+# If all those fields have equal values for two given benchmarks, then the oldest
+# benchmark (given by RUN_DATE) will be discarded
+UNIQUE_BENCHMARK_KEY = [
+    BENCHMARK_ID_NAME,
+    DTYPE,
+    NB_DATA_SAMPLES,
+    NB_DATA_FEATURES,
+    NB_DATA_TARGETS,
+    DATA_SAMPLE_WEIGHTS,
+    BACKEND_PROVIDER,
+    SOLVER,
+    COMPUTE_DEVICE,
+    COMPUTE_RUNTIME,
+    PLATFORM,
+    PLATFORM_ARCHITECTURE,
+    SYSTEM_PROCESSOR,
+    SYSTEM_CPUS,
+    SYSTEM_GPU,
+    DATA_RANDOM_STATE,
+    SOLVER_RANDOM_STATE,
+]
+
+# Importance and say if ascending / descending
+ROW_SORT_ORDER = [
+    (DTYPE, True),
+    (NB_DATA_SAMPLES, False),
+    (NB_DATA_FEATURES, False),
+    (NB_DATA_TARGETS, False),
+    (DATA_SAMPLE_WEIGHTS, True),
+    (ALPHA, False),
+    (WALLTIME, True),
+    (BACKEND_PROVIDER, True),
+    (COMPUTE_DEVICE, True),
+    (COMPUTE_RUNTIME, True),
+    (RESULT_NB_ITERATIONS, True),
+    (OBJECTIVE_FUNCTION_VALUE, False),
+    (SOLVER, True),
+    (SYSTEM_GPU, True),
+    (SYSTEM_CPUS, True),
+    (PLATFORM, True),
+    (PLATFORM_ARCHITECTURE, True),
+    (PLATFORM_RELEASE, True),
+    (SYSTEM_PROCESSOR, True),
+    (SYSTEM_RAM, True),
+    (DATA_RANDOM_STATE, True),
+    (SOLVER_RANDOM_STATE, True),
+    (RUN_DATE, False),
+    (VERSION_INFO, False),
+    (COMMENT, True),
+    (BENCHMARK_ID_NAME, True),
+]
+_row_sort_by, _row_sort_ascending = map(list, zip(*ROW_SORT_ORDER))
+
+PARQUET_TABLE_DISPLAY_MAPPING = dict(
+    time=WALLTIME,
+    objective_value=OBJECTIVE_FUNCTION_VALUE,
+    objective_n_iter=RESULT_NB_ITERATIONS,
+    objective_dataset_param_n_samples=NB_DATA_SAMPLES,
+    objective_dataset_param_n_features=NB_DATA_FEATURES,
+    objective_dataset_param_n_targets=NB_DATA_TARGETS,
+    objective_dataset_param_dtype=DTYPE,
+    objective_dataset_param_random_state=DATA_RANDOM_STATE,
+    objective_objective_param_alpha=ALPHA,
+    objective_objective_param_fit_intercept=ALPHA,
+    objective_objective_param_alpha=ALPHA,
+    objective_objective_param_random_state=SOLVER_RANDOM_STATE,
+    objective_solver_param___name=BACKEND_PROVIDER,
+    objective_solver_param_device=COMPUTE_DEVICE,
+    objective_solver_param_runtime=COMPUTE_RUNTIME,
+    objective_solver_param_solver=SOLVER,
+    objective_solver_param_comment=COMMENT,
+    objective_solver_param_version_info=VERSION_INFO,
+    objective_solver_param_run_date=RUN_DATE,
+    platform=PLATFORM,
+)
+
+PARQUET_TABLE_DISPLAY_MAPPING.update(
+    {
+        "platform-architecture": PLATFORM_ARCHITECTURE,
+        "platform-release": PLATFORM_RELEASE,
+        "system-cpus": SYSTEM_CPUS,
+        "system-processor": SYSTEM_PROCESSOR,
+        "system-ram (GB)": SYSTEM_RAM,
+    }
+)
+_all_table_columns = list(PARQUET_TABLE_DISPLAY_MAPPING) + [BENCHMARK_ID_NAME]
+
+ALL_EXPECTED_COLUMNS = set(BENCHMARK_DEFINING_COLUMNS + _all_table_columns)
+
+IDS_LENGTH = 8
+
+
+def _get_id_from_str(s):
+    return hashlib.sha256(s.encode("utf8"), usedforsecurity=False).hexdigest()[
+        :IDS_LENGTH
+    ]
+
+
+def _get_sample_id_for_columns(row, defining_colums, constant_identifier):
+    return _get_id_from_str(
+        "".join(row[defining_colums].astype(str)) + constant_identifier
+    )
+
+
+def _validate_one_parquet_table(source):
+    df = pd.read_parquet(source)
+
+    # NB: we're lenient on the columns
+    for col in ALL_EXPECTED_COLUMNS - set(df.columns):
+        df[col] = None
+
+    df[BENCHMARK_ID_NAME] = df.apply(
+        lambda row: _get_sample_id_for_columns(
+            row, BENCHMARK_DEFINING_COLUMNS, _benchmark_defining_columns_identifier
+        ),
+        axis=1,
+    )
+
+    df = df[_all_table_columns]
+    df.rename(columns=PARQUET_TABLE_DISPLAY_MAPPING, inplace=True, errors="raise")
+
+    df[RUN_DATE] = df[RUN_DATE].astype("datetime64[ns]")
+
+    return df
+
+
+def _validate_one_csv_table(source, parse_dates=True, order_columns=True):
+    NA_VALUES = set(STR_NA_VALUES)
+    NA_VALUES.discard("None")
+
+    df = pd.read_csv(
+        source,
+        usecols=TABLE_DISPLAY_ORDER,
+        dtype=COLUMNS_DTYPES,
+        index_col=False,
+        na_values={col: NA_VALUES for col in COLUMNS_WITH_NONE_STRING},
+        keep_default_na=False,
+    )
+
+    if order_columns:
+        df = df[TABLE_DISPLAY_ORDER]
+
+    if parse_dates:
+        df[RUN_DATE] = pd.to_datetime(df[RUN_DATE], format=DATES_FORMAT).astype(
+            "datetime64[ns]"
+        )
+
+    return df
+
+
+def _assemble_output_table(
+    dfs_from_csv, dfs_from_parquet, parquet_gpu_name, create_gpu_entry, list_known_gpus
+):
+
+    if not list_known_gpus and (len(dfs_from_parquet) == 0):
+        if parquet_gpu_name is not None:
+            parameter_name = (
+                "--parquet-gpu-name" if parquet_gpu_name else "--no-parquet-gpu-name"
+            )
+            raise ValueError(
+                f"The parameter {parameter_name} should only be used if at least one "
+                "benchopt parquet table is being consolidated, but only got csv tables."
+            )
+        if create_gpu_entry is not False:
+            raise ValueError(
+                "The parameter --create-gpu-entry should only be used if at least one "
+                "benchopt parquet table is being consolidated, but got only csv tables."
+            )
+    elif not list_known_gpus and parquet_gpu_name is None:
+        raise ValueError(
+            "Please use the --parquet-gpu-name parameter to provide a gpu name that "
+            "will be added to the metadata of the samples in the input parquet tables "
+            "or use the --no-parquet-gpu-name if you intend to leave the corresponding "
+            "field empty."
+        )
+
+    else:
+        gpu_names_from_csv = set(
+            gpu_name
+            for df in dfs_from_csv
+            for gpu_name in df[SYSTEM_GPU]
+            if (len(gpu_name) > 0)
+        )
+
+        if list_known_gpus:
+            print("\n".join(gpu_names_from_csv))
+            return False
+
+        if (
+            (len(parquet_gpu_name) > 0)
+            and (parquet_gpu_name not in gpu_names_from_csv)
+            and not create_gpu_entry
+        ):
+            raise IndexError(
+                f"The gpu name {parquet_gpu_name} is unknown. Please use the "
+                "--new-gpu-entry parameter to confirm the addition of the new gpu "
+                "entry in the output csv table, or use --list-known-gpus parameter to "
+                "print a list of gpus names that have been already registered and use "
+                "one of those to bypass this error."
+            )
+
+        for df in dfs_from_parquet:
+            df[SYSTEM_GPU] = parquet_gpu_name
+
+    df_list = dfs_from_csv + dfs_from_parquet
+
+    if len(df_list) > 1:
+        df = pd.concat(df_list, ignore_index=True, copy=False)
+    else:
+        df = df_list[0]
+
+    df = df[TABLE_DISPLAY_ORDER]
+    df.sort_values(
+        by=_row_sort_by, ascending=_row_sort_ascending, inplace=True, kind="stable"
+    )
+    # HACK: sanitize mix of None values and empty strings that can happen when some
+    # columns are missing in the parquet input files (because it's optional and no
+    # solver returns it in the batch) by passing the data to CSV and re-loading
+    # again from CSV
+    df = _sanitize_df_with_tocsv(df)
+
+    df.drop_duplicates(subset=UNIQUE_BENCHMARK_KEY, inplace=True, ignore_index=True)
+
+    return df
+
+
+def _sanitize_df_with_tocsv(df):
+    in_memory_buffer = BytesIO()
+    _df_to_csv(df, in_memory_buffer)
+    in_memory_buffer.seek(0)
+    return _validate_one_csv_table(in_memory_buffer, order_columns=False)
+
+
+def _df_to_csv(df, target):
+    float_format_fn = partial(
+        np.format_float_positional,
+        precision=3,
+        unique=True,
+        fractional=False,
+        trim="-",
+        sign=False,
+        pad_left=None,
+        pad_right=None,
+        min_digits=None,
+    )
+    df = df.copy()
+    df[WALLTIME] = df[WALLTIME].map(float_format_fn)
+    df.to_csv(
+        target,
+        index=False,
+        mode="a",
+        date_format=DATES_FORMAT,
+    )
+
+
+def _gspread_sync(source, gspread_url, gspread_auth_key):
+    import gspread
+
+    df = _validate_one_csv_table(source, parse_dates=False)
+
+    n_rows, n_cols = df.shape
+    walltime_worksheet_col = df.columns.get_loc(WALLTIME) + 1
+
+    gs = gspread.service_account(gspread_auth_key)
+    sheet = gs.open_by_url(gspread_url)
+
+    global_range = (
+        f"{gspread.utils.rowcol_to_a1(1, 1)}:"
+        f"{gspread.utils.rowcol_to_a1(n_rows + 1, n_cols)}"
+    )
+
+    try:
+        worksheet = sheet.worksheet(GOOGLE_WORKSHEET_NAME)
+        worksheet.clear()
+        worksheet.clear_basic_filter()
+        worksheet.freeze(0, 0)
+        worksheet.resize(rows=n_rows + 1, cols=n_cols)
+        worksheet.clear_notes(global_range)
+        white_background = dict(
+            backgroundColorStyle=dict(rgbColor=dict(red=1, green=1, blue=1, alpha=1))
+        )
+        worksheet.format(global_range, white_background)
+    except gspread.WorksheetNotFound:
+        worksheet = sheet.add_worksheet(
+            GOOGLE_WORKSHEET_NAME, rows=n_rows + 1, cols=n_cols
+        )
+        # ensure worksheets are sorted anti-alphabetically
+        sheet.reorder_worksheets(
+            sorted(sheet.worksheets(), key=attrgetter("title"), reverse=True)
+        )
+
+    # upload all values
+    worksheet.update(
+        values=[df.columns.values.tolist()] + df.values.tolist(), range_name="A1"
+    )
+
+    # set filter
+    worksheet.set_basic_filter(1, 1, n_rows + 1, n_cols)
+
+    # freeze filter rows and benchmark-defining cols
+    worksheet.freeze(rows=1, cols=walltime_worksheet_col)
+
+    format_queries = []
+
+    # Text is centerd and wrapped in all cells
+    global_format = dict(
+        horizontalAlignment="CENTER",
+        verticalAlignment="MIDDLE",
+        wrapStrategy="WRAP",
+    )
+    format_queries.append(dict(range=global_range, format=global_format))
+
+    # benchmark_id and walltime columns are bold
+    bold_format = dict(textFormat=dict(bold=True))
+    benchmark_id_col_range = (
+        f"{gspread.utils.rowcol_to_a1(2, 1)}:"
+        f"{gspread.utils.rowcol_to_a1(n_rows + 1, 1)}"
+    )
+    walltime_col_range = (
+        f"{gspread.utils.rowcol_to_a1(2, walltime_worksheet_col)}:"
+        f"{gspread.utils.rowcol_to_a1(n_rows + 1, walltime_worksheet_col)}"
+    )
+    format_queries.append(dict(range=benchmark_id_col_range, format=bold_format))
+    format_queries.append(dict(range=walltime_col_range, format=bold_format))
+
+    # Header is light-ish yellow
+    yellow_lighter_header = dict(
+        backgroundColorStyle=dict(
+            rgbColor=dict(red=1, green=1, blue=102 / 255, alpha=1)
+        )
+    )
+    header_row_range = (
+        f"{gspread.utils.rowcol_to_a1(1, 1)}:"
+        f"{gspread.utils.rowcol_to_a1(1, n_cols)}"
+    )
+    format_queries.append(dict(range=header_row_range, format=yellow_lighter_header))
+
+    # Every other benchmark_id has greyed background
+    bright_gray_background = dict(
+        backgroundColorStyle=dict(
+            rgbColor=dict(red=232 / 255, green=233 / 255, blue=235 / 255, alpha=1)
+        )
+    )
+    benchmark_ids = df[BENCHMARK_ID_NAME]
+    benchmark_ids_ending_idx = (
+        np.where((benchmark_ids.shift() != benchmark_ids).values[1:])[0] + 2
+    )
+    for benchmark_id_range_start, benchmark_id_range_end in zip_longest(
+        *(iter(benchmark_ids_ending_idx),) * 2
+    ):
+        benchmark_row_range = (
+            f"{gspread.utils.rowcol_to_a1(benchmark_id_range_start + 1, 1)}:"
+            f"{gspread.utils.rowcol_to_a1(benchmark_id_range_end or (n_rows + 1), n_cols)}"  # noqa
+        )
+        format_queries.append(
+            dict(range=benchmark_row_range, format=bright_gray_background)
+        )
+
+    # Apply formats
+    worksheet.batch_format(format_queries)
+
+    # auto-resize rows and cols
+    worksheet.columns_auto_resize(0, n_cols - 1)
+    worksheet.rows_auto_resize(0, n_rows)
+
+
+if __name__ == "__main__":
+    import os
+    import sys
+    from argparse import ArgumentParser
+
+    argparser = ArgumentParser(
+        description=(
+            "Print an aggregated CSV-formated database of ridge benchmark results "
+            "for the sklearn-engine-benchmarks project hosted at "
+            "https://github.com/soda-inria/sklearn-engine-benchmarks.\n\n"
+            "The inputs are assumed to be a collection of benchopt parquet files and "
+            "CSV files, well formated according to the project current specs. This "
+            "command assumes rhat the inputs are valid and is lenient at checking "
+            "types, null values, or missing columns, hence the user is advised to "
+            "cautiously check outputs before using.\n\n"
+            "If several results are found for identical benchmarks, only the most "
+            "recent `Run date` value is retained, all anterior entries are discarded "
+            "from the output CSV."
+        )
+    )
+
+    argparser.add_argument(
+        "benchmark_files",
+        nargs="+",
+        help="benchopt parquet files or sklearn-engine-benchmarks csv files",
+    )
+
+    argparser.add_argument(
+        "--check-csv",
+        action="store_true",
+        help="Perform a few sanity checks on a CSV database of ridge benchmark "
+        "results. If this option is passed, then the command only expects a single "
+        "input path to a csv file.",
+    )
+
+    argparser.add_argument(
+        "--sync-to-gspread",
+        action="store_true",
+        help="Synchronize a CSV database of ridge benchmark results to a google "
+        "spreadsheet and format it nicely. If this option is passed, then the command "
+        "only expects a single input path to a csv file, and also requires "
+        "--gspread-url and --gspread-auth-key.",
+    )
+
+    argparser.add_argument(
+        "--gspread-url",
+        help="URL to a google spreadsheet. Expected if and only if --sync-to-gspread "
+        "is passed.",
+    )
+
+    argparser.add_argument(
+        "--gspread-auth-key",
+        help="Path to a json authentication key for a gspread service account. "
+        "Expected if and only if --sync-to-gspread is passed.",
+    )
+
+    argparser.add_argument(
+        "--parquet-gpu-name",
+        help="Name of the GPU on the host that runs the benchmarks that are recorded "
+        "in the input parquet files.",
+    )
+
+    argparser.add_argument(
+        "--no-parquet-gpu-name",
+        action="store_true",
+        help="Do not insert a GPU name in the metadata of the benchmark samples that "
+        "were recorded in the input parquet files (and leave it blank).",
+    )
+
+    argparser.add_argument(
+        "--new-gpu-entry",
+        action="store_true",
+        help="Use this parameter along with --parquet-gpu-name to confirm that if the "
+        "GPU name is not yet known in the existing databases, it will be added to the "
+        "list of known GPU names. Else the command will throw an error.",
+    )
+
+    argparser.add_argument(
+        "--list-known-gpus",
+        action="store_true",
+        help="Will print a list of the GPU names that are used in CSV benchmark files.",
+    )
+
+    args = argparser.parse_args()
+
+    if (parquet_gpu_name := args.parquet_gpu_name) is None and args.no_parquet_gpu_name:
+        parquet_gpu_name = ""
+
+    create_gpu_entry = args.new_gpu_entry
+    list_known_gpus = args.list_known_gpus
+
+    paths = args.benchmark_files
+    if (check_csv := args.check_csv) or args.sync_to_gspread:
+        if (n_paths := len(paths)) > 1:
+            command = "--check-csv" if check_csv else "--sync-to-gspread"
+            raise ValueError(
+                f"A single input path to a csv file is expected when the {command} "
+                f"parameter is passed, but you passed {n_paths - 1} additional "
+                "arguments."
+            )
+        path = paths[0]
+        _, file_extension = os.path.splitext(path)
+        if file_extension != ".csv":
+            raise ValueError(
+                "Expecting a '.csv' file extensions, but got "
+                f"{file_extension} instead !"
+            )
+
+    if check_csv:
+        df_loaded = _validate_one_csv_table(path)
+        df_clean = _assemble_output_table(
+            dfs_from_csv=[df_loaded],
+            dfs_from_parquet=[],
+            parquet_gpu_name=None,
+            create_gpu_entry=False,
+            list_known_gpus=list_known_gpus,
+        )
+
+        pd.testing.assert_frame_equal(df_loaded, df_clean)
+
+    if gspread_sync := args.sync_to_gspread:
+        if (gspread_url := args.gspread_url) is None:
+            raise ValueError(
+                "Please provide a URL to a google spreadsheet using the "
+                "--gspread-url parameter."
+            )
+
+        if (gspread_auth_key := args.gspread_auth_key) is None:
+            raise ValueError(
+                "Please use the --gspread-auth-key parameter to pass a json "
+                "authentication key for a service account from the google developer "
+                "console."
+            )
+        _gspread_sync(path, gspread_url, gspread_auth_key)
+
+    if not check_csv and not gspread_sync:
+        dfs_from_parquet, dfs_from_csv = [], []
+        for path in paths:
+            _, file_extension = os.path.splitext(path)
+            if file_extension == ".parquet":
+                if list_known_gpus:
+                    continue
+                dfs_from_parquet.append(_validate_one_parquet_table(path))
+            elif file_extension == ".csv":
+                dfs_from_csv.append(_validate_one_csv_table(path, order_columns=False))
+            else:
+                raise ValueError(
+                    "Expecting '.csv' or '.parquet' file extensions, but got "
+                    f"{file_extension} instead !"
+                )
+
+        df = _assemble_output_table(
+            dfs_from_csv=dfs_from_csv,
+            dfs_from_parquet=dfs_from_parquet,
+            parquet_gpu_name=parquet_gpu_name,
+            create_gpu_entry=create_gpu_entry,
+            list_known_gpus=list_known_gpus,
+        )
+
+        if df is not False:
+            _df_to_csv(df, sys.stdout)
diff --git a/benchmarks/ridge/datasets/simulated_blobs.py b/benchmarks/ridge/datasets/simulated_blobs.py
new file mode 100644
index 0000000..0ef5605
--- /dev/null
+++ b/benchmarks/ridge/datasets/simulated_blobs.py
@@ -0,0 +1,41 @@
+from benchopt import BaseDataset, safe_import_context
+from benchopt.datasets import make_correlated_data
+
+with safe_import_context() as import_ctx:
+    import numpy as np
+
+
+class Dataset(BaseDataset):
+    name = "Simulated_correlated_data"
+
+    parameters = {
+        "n_samples, n_features": [
+            (20_000_000, 100),
+            (15_000, 15_000),
+            (2_000_000, 100),
+            (5000, 5000),
+        ],
+        "n_targets": [1, 5, 20],
+        "dtype": ["float32"],
+        "random_state": [123],
+    }
+
+    def __init__(self, n_samples, n_features, n_targets, dtype, random_state):
+        self.n_samples = n_samples
+        self.n_features = n_features
+        self.n_targets = n_targets
+        self.random_state = random_state
+        self.dtype = dtype
+
+    def get_data(self):
+        rng = np.random.RandomState(self.random_state)
+
+        X, y = make_correlated_data(
+            self.n_samples, self.n_features, self.n_targets, random_state=rng
+        )
+
+        dtype = getattr(np, self.dtype)
+
+        return dict(
+            X=X.astype(dtype), y=y.astype(dtype), __name=self.name, **self._parameters
+        )
diff --git a/benchmarks/ridge/objective.py b/benchmarks/ridge/objective.py
new file mode 100644
index 0000000..67c8708
--- /dev/null
+++ b/benchmarks/ridge/objective.py
@@ -0,0 +1,86 @@
+from datetime import datetime
+
+from benchopt import BaseObjective, safe_import_context
+
+with safe_import_context() as import_ctx:
+    import numpy as np
+
+
+class Objective(BaseObjective):
+    name = "Ridge walltime"
+    url = "https://github.com/soda-inria/sklearn-engine-benchmarks"
+
+    requirements = ["numpy"]
+
+    # Since our goal is to measure walltime for solvers that perform exact same
+    # computations, the solver parameters are part of the objective and must be set
+    # for all solvers, rather than being an independent benchmark space for each
+    # solver.
+    parameters = {
+        "alpha": [1.0],
+        "fit_intercept": [True],
+        "solver, max_iter, tol": [("svd", None, 0)],
+        "sample_weight": ["None", "random"],
+        "random_state": [123],
+    }
+
+    def set_data(self, X, **dataset_parameters):
+        self.X = X
+        dtype = X.dtype
+
+        if self.sample_weight == "None":
+            sample_weight = None
+        elif self.sample_weight == "unary":
+            sample_weight = np.ones(len(X), dtype=dtype)
+        elif self.sample_weight == "random":
+            rng_sample_weight = np.random.default_rng(
+                dataset_parameters["sample_weight"] + 1
+            )
+            sample_weight = rng_sample_weight.random(size=len(X)).astype(dtype)
+        else:
+            raise ValueError(
+                "Expected 'sample_weight' parameter to be either equal to 'None', "
+                f"'unary' or 'random', but got {sample_weight}."
+            )
+
+        self.sample_weight_ = sample_weight
+        self.dataset_parameters = dataset_parameters
+
+    def evaluate_result(self, objective, **solver_parameters):
+        all_parameters = dict(solver_param_run_date=datetime.today())
+        all_parameters.update(
+            {
+                ("dataset_param_" + key): value
+                for key, value in self.dataset_parameters.items()
+            }
+        )
+        all_parameters.update(
+            {
+                ("objective_param_" + key): value
+                for key, value in self._parameters.items()
+            }
+        )
+        all_parameters.update(
+            {("solver_param_" + key): value for key, value in solver_parameters.items()}
+        )
+        return dict(
+            value=objective,
+            objective_param___name=self.name,
+            **all_parameters,
+        )
+
+    def get_one_result(self):
+        return dict(objective=1)
+
+    def get_objective(self):
+        return dict(
+            X=self.X,
+            y=self.y,
+            sample_weight=self.sample_weight_,
+            alpha=self.alpha,
+            fit_intercept=self.fit_intercept,
+            solver=self.solver,
+            max_iter=self.max_iter,
+            tol=self.tol,
+            random_state=self.random_state,
+        )
diff --git a/benchmarks/ridge/solvers/scikit_learn.py b/benchmarks/ridge/solvers/scikit_learn.py
new file mode 100644
index 0000000..d60f41f
--- /dev/null
+++ b/benchmarks/ridge/solvers/scikit_learn.py
@@ -0,0 +1,76 @@
+from importlib.metadata import version
+
+from benchopt import BaseSolver, safe_import_context
+from benchopt.stopping_criterion import SingleRunCriterion
+
+with safe_import_context() as import_ctx:
+    from sklearn.linear_model import Ridge
+    from sklearn.linear_model._base import _rescale_data
+
+
+class Solver(BaseSolver):
+    name = "scikit-learn"
+    requirements = ["scikit-learn"]
+
+    stopping_criterion = SingleRunCriterion(1)
+
+    def set_objective(
+        self,
+        X,
+        y,
+        sample_weight,
+        alpha,
+        fit_intercept,
+        solver,
+        max_iter,
+        tol,
+        random_state,
+    ):
+        # Copy the data before running the benchmark to ensure that no unfortunate side
+        # effects can happen
+        self.X = X.copy()
+        self.y = y.copy()
+
+        if hasattr(sample_weight, "copy"):
+            sample_weight = sample_weight.copy()
+        self.sample_weight = sample_weight
+
+        self.alpha = alpha
+        self.fit_intercept = fit_intercept
+        self.solver = solver
+        self.max_iter = max_iter
+        self.tol = tol
+        self.random_state = random_state
+
+    def run(self, _):
+        estimator = Ridge(
+            alpha=self.alpha,
+            fit_intercept=self.fit_intercept,
+            copy_X=False,
+            max_iter=self.max_iter,
+            tol=self.tol,
+            solver=self.solver,
+            positive=True if (self.solver == "lbfgs") else False,
+            random_state=self.random_state,
+        ).fit(self.X, self.y, self.sample_weight)
+
+        self.coef_ = estimator.coef_
+
+    def get_result(self):
+        if self.sample_weight is not None:
+            X, y, _ = _rescale_data(self.X, self.y, self.sample_weight, inplace=True)
+
+        y = y.reshape((y.shape[0], -1))
+
+        coef_ = self.coef_.reshape((X.shape[0], 1, -1))
+
+        objective = ((y - (X @ coef_).squeeze(1)) ** 2).sum() + (
+            len(y.T) * self.alpha * (coef_**2).sum()
+        )
+
+        return dict(
+            objective=objective,
+            version_info=f"scikit-learn {version('scikit-learn')}",
+            __name=self.name,
+            **self._parameters,
+        )

From 08cc70f1c90e3770210fb04d45a3a865b325f94a Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Thu, 11 Jan 2024 15:10:31 +0100
Subject: [PATCH 02/43] scikit-learn benchmark works

---
 benchmarks/kmeans/objective.py               | 2 +-
 benchmarks/ridge/datasets/simulated_blobs.py | 2 +-
 benchmarks/ridge/objective.py                | 5 +++--
 benchmarks/ridge/solvers/scikit_learn.py     | 7 ++++---
 4 files changed, 9 insertions(+), 7 deletions(-)

diff --git a/benchmarks/kmeans/objective.py b/benchmarks/kmeans/objective.py
index c3e860c..942f701 100644
--- a/benchmarks/kmeans/objective.py
+++ b/benchmarks/kmeans/objective.py
@@ -38,7 +38,7 @@ def set_data(self, X, **dataset_parameters):
             sample_weight = np.ones(len(X), dtype=dtype)
         elif self.sample_weight == "random":
             rng_sample_weight = np.random.default_rng(
-                dataset_parameters["sample_weight"] + 1
+                dataset_parameters["random_state"] + 1
             )
             sample_weight = rng_sample_weight.random(size=len(X)).astype(dtype)
         else:
diff --git a/benchmarks/ridge/datasets/simulated_blobs.py b/benchmarks/ridge/datasets/simulated_blobs.py
index 0ef5605..a4e3832 100644
--- a/benchmarks/ridge/datasets/simulated_blobs.py
+++ b/benchmarks/ridge/datasets/simulated_blobs.py
@@ -30,7 +30,7 @@ def __init__(self, n_samples, n_features, n_targets, dtype, random_state):
     def get_data(self):
         rng = np.random.RandomState(self.random_state)
 
-        X, y = make_correlated_data(
+        X, y, _ = make_correlated_data(
             self.n_samples, self.n_features, self.n_targets, random_state=rng
         )
 
diff --git a/benchmarks/ridge/objective.py b/benchmarks/ridge/objective.py
index 67c8708..40b2713 100644
--- a/benchmarks/ridge/objective.py
+++ b/benchmarks/ridge/objective.py
@@ -24,8 +24,9 @@ class Objective(BaseObjective):
         "random_state": [123],
     }
 
-    def set_data(self, X, **dataset_parameters):
+    def set_data(self, X, y, **dataset_parameters):
         self.X = X
+        self.y = y
         dtype = X.dtype
 
         if self.sample_weight == "None":
@@ -34,7 +35,7 @@ def set_data(self, X, **dataset_parameters):
             sample_weight = np.ones(len(X), dtype=dtype)
         elif self.sample_weight == "random":
             rng_sample_weight = np.random.default_rng(
-                dataset_parameters["sample_weight"] + 1
+                dataset_parameters["random_state"] + 1
             )
             sample_weight = rng_sample_weight.random(size=len(X)).astype(dtype)
         else:
diff --git a/benchmarks/ridge/solvers/scikit_learn.py b/benchmarks/ridge/solvers/scikit_learn.py
index d60f41f..dec54da 100644
--- a/benchmarks/ridge/solvers/scikit_learn.py
+++ b/benchmarks/ridge/solvers/scikit_learn.py
@@ -57,14 +57,15 @@ def run(self, _):
         self.coef_ = estimator.coef_
 
     def get_result(self):
+        X, y = self.X, self.y
         if self.sample_weight is not None:
-            X, y, _ = _rescale_data(self.X, self.y, self.sample_weight, inplace=True)
+            X, y, _ = _rescale_data(X, y, self.sample_weight, inplace=True)
 
         y = y.reshape((y.shape[0], -1))
 
-        coef_ = self.coef_.reshape((X.shape[0], 1, -1))
+        coef_ = self.coef_.reshape((-1, X.shape[1], 1))
 
-        objective = ((y - (X @ coef_).squeeze(1)) ** 2).sum() + (
+        objective = ((y.T - (X @ coef_).squeeze(2)) ** 2).sum() + (
             len(y.T) * self.alpha * (coef_**2).sum()
         )
 

From 9de71549811ec43192d71d9195e70604f4626b7a Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Thu, 11 Jan 2024 17:01:15 +0100
Subject: [PATCH 03/43] smaller dimensions / consolidation script works

---
 benchmarks/ridge/consolidate_result_csv.py   | 16 +++++++++-------
 benchmarks/ridge/datasets/simulated_blobs.py |  8 ++++----
 2 files changed, 13 insertions(+), 11 deletions(-)

diff --git a/benchmarks/ridge/consolidate_result_csv.py b/benchmarks/ridge/consolidate_result_csv.py
index 3f780e9..fd5cc2f 100644
--- a/benchmarks/ridge/consolidate_result_csv.py
+++ b/benchmarks/ridge/consolidate_result_csv.py
@@ -50,7 +50,9 @@
 SYSTEM_PROCESSOR = "Cpu name"
 SYSTEM_RAM = "RAM (GB)"
 SYSTEM_GPU = "Gpu name"
-RESULT_NB_ITERATIONS = "Result nb iterations"
+# TODO: add number of iterations once iteration-based solvers are
+# added to the benchmark.
+# RESULT_NB_ITERATIONS = "Result nb iterations"
 OBJECTIVE_FUNCTION_VALUE = "Result objective value"
 VERSION_INFO = "Version info"
 RUN_DATE = "Run date"
@@ -82,7 +84,7 @@
     RUN_DATE,
     VERSION_INFO,
     COMMENT,
-    RESULT_NB_ITERATIONS,
+    # RESULT_NB_ITERATIONS,
     OBJECTIVE_FUNCTION_VALUE,
     DATA_RANDOM_STATE,
     SOLVER_RANDOM_STATE,
@@ -95,11 +97,12 @@
     NB_DATA_FEATURES: np.int64,
     NB_DATA_TARGETS: np.int64,
     ALPHA: np.float64,
+    DATA_SAMPLE_WEIGHTS: str,
     WALLTIME: np.float64,
     BACKEND_PROVIDER: str,
     COMPUTE_DEVICE: str,
     COMPUTE_RUNTIME: str,
-    RESULT_NB_ITERATIONS: np.int64,
+    # RESULT_NB_ITERATIONS: np.int64,
     OBJECTIVE_FUNCTION_VALUE: np.float64,
     SOLVER: str,
     PLATFORM: str,
@@ -152,7 +155,7 @@
     (BACKEND_PROVIDER, True),
     (COMPUTE_DEVICE, True),
     (COMPUTE_RUNTIME, True),
-    (RESULT_NB_ITERATIONS, True),
+    # (RESULT_NB_ITERATIONS, True),
     (OBJECTIVE_FUNCTION_VALUE, False),
     (SOLVER, True),
     (SYSTEM_GPU, True),
@@ -174,16 +177,15 @@
 PARQUET_TABLE_DISPLAY_MAPPING = dict(
     time=WALLTIME,
     objective_value=OBJECTIVE_FUNCTION_VALUE,
-    objective_n_iter=RESULT_NB_ITERATIONS,
+    # objective_n_iter=RESULT_NB_ITERATIONS,
     objective_dataset_param_n_samples=NB_DATA_SAMPLES,
     objective_dataset_param_n_features=NB_DATA_FEATURES,
     objective_dataset_param_n_targets=NB_DATA_TARGETS,
     objective_dataset_param_dtype=DTYPE,
     objective_dataset_param_random_state=DATA_RANDOM_STATE,
     objective_objective_param_alpha=ALPHA,
-    objective_objective_param_fit_intercept=ALPHA,
-    objective_objective_param_alpha=ALPHA,
     objective_objective_param_random_state=SOLVER_RANDOM_STATE,
+    objective_objective_param_sample_weight=DATA_SAMPLE_WEIGHTS,
     objective_solver_param___name=BACKEND_PROVIDER,
     objective_solver_param_device=COMPUTE_DEVICE,
     objective_solver_param_runtime=COMPUTE_RUNTIME,
diff --git a/benchmarks/ridge/datasets/simulated_blobs.py b/benchmarks/ridge/datasets/simulated_blobs.py
index a4e3832..d8d178e 100644
--- a/benchmarks/ridge/datasets/simulated_blobs.py
+++ b/benchmarks/ridge/datasets/simulated_blobs.py
@@ -10,10 +10,10 @@ class Dataset(BaseDataset):
 
     parameters = {
         "n_samples, n_features": [
-            (20_000_000, 100),
-            (15_000, 15_000),
-            (2_000_000, 100),
-            (5000, 5000),
+            (200_000, 100),
+            (1500, 1500),
+            (200_000, 10),
+            (500, 500),
         ],
         "n_targets": [1, 5, 20],
         "dtype": ["float32"],

From d1d8760581c524aca4be0b19152c72c3d8697b0b Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Fri, 12 Jan 2024 11:06:28 +0100
Subject: [PATCH 04/43] add results.csv file

---
 benchmarks/ridge/results.csv | 25 +++++++++++++++++++++++++
 1 file changed, 25 insertions(+)
 create mode 100644 benchmarks/ridge/results.csv

diff --git a/benchmarks/ridge/results.csv b/benchmarks/ridge/results.csv
new file mode 100644
index 0000000..68de113
--- /dev/null
+++ b/benchmarks/ridge/results.csv
@@ -0,0 +1,25 @@
+Benchmark id,Dtype,Nb data samples,Nb data features,Nb data targets,Data sample weights,alpha,Walltime,Backend provider,Compute device,Compute runtime,Solver,Nb cpus,Cpu name,Gpu name,RAM (GB),Platform,Platform architecture,Platform release,Run date,Version info,Comment,Result objective value,Data random state,Solver random state
+3e6d9fa7,float32,200000,100,20,None,1.0,0.313,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,9653625.760253906,123,123
+e7a310cb,float32,200000,100,20,random,1.0,0.355,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,4837163.777099609,123,123
+717e20b6,float32,200000,100,5,None,1.0,0.292,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,2390454.421310425,123,123
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From 893f67e58475d49ce2b177dc891384d6c15b148b Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Fri, 12 Jan 2024 11:58:20 +0100
Subject: [PATCH 05/43] Increase dimensions / update results

---
 benchmarks/ridge/datasets/simulated_blobs.py  |   7 ++-
 .../benchopt_run_2024-01-12_11h52m30.parquet  | Bin 0 -> 32880 bytes
 benchmarks/ridge/results.csv                  |  52 ++++++++++--------
 3 files changed, 33 insertions(+), 26 deletions(-)
 create mode 100644 benchmarks/ridge/outputs/benchopt_run_2024-01-12_11h52m30.parquet

diff --git a/benchmarks/ridge/datasets/simulated_blobs.py b/benchmarks/ridge/datasets/simulated_blobs.py
index d8d178e..e8db00c 100644
--- a/benchmarks/ridge/datasets/simulated_blobs.py
+++ b/benchmarks/ridge/datasets/simulated_blobs.py
@@ -10,10 +10,11 @@ class Dataset(BaseDataset):
 
     parameters = {
         "n_samples, n_features": [
-            (200_000, 100),
+            (20_000_000, 100),
+            (5000, 5000),
+            (20_000_000, 10),
+            (2_000_000, 100),
             (1500, 1500),
-            (200_000, 10),
-            (500, 500),
         ],
         "n_targets": [1, 5, 20],
         "dtype": ["float32"],
diff --git a/benchmarks/ridge/outputs/benchopt_run_2024-01-12_11h52m30.parquet b/benchmarks/ridge/outputs/benchopt_run_2024-01-12_11h52m30.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..4d45266494158aaeb442d33412c98af44a8fc6af
GIT binary patch
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diff --git a/benchmarks/ridge/results.csv b/benchmarks/ridge/results.csv
index 68de113..b849b5e 100644
--- a/benchmarks/ridge/results.csv
+++ b/benchmarks/ridge/results.csv
@@ -1,25 +1,31 @@
 Benchmark id,Dtype,Nb data samples,Nb data features,Nb data targets,Data sample weights,alpha,Walltime,Backend provider,Compute device,Compute runtime,Solver,Nb cpus,Cpu name,Gpu name,RAM (GB),Platform,Platform architecture,Platform release,Run date,Version info,Comment,Result objective value,Data random state,Solver random state
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-e7a310cb,float32,200000,100,20,random,1.0,0.355,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,4837163.777099609,123,123
-717e20b6,float32,200000,100,5,None,1.0,0.292,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,2390454.421310425,123,123
-34fcc214,float32,200000,100,5,random,1.0,0.341,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,1199593.1394348145,123,123
-f9ad376b,float32,200000,100,1,None,1.0,0.537,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,667870.030544281,123,123
-bd618e32,float32,200000,100,1,random,1.0,0.329,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,335730.99897003174,123,123
-b1164905,float32,200000,10,20,None,1.0,0.0513,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,978816.2872009276,123,123
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-63405b3d,float32,1500,1500,1,None,1.0,0.432,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,2250.33251953125,123,123
-784cd81e,float32,1500,1500,1,random,1.0,0.412,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,1258.864501953125,123,123
-4fdd16bc,float32,500,500,20,None,1.0,0.0515,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,101646.53369140624,123,123
-48999078,float32,500,500,20,random,1.0,0.0718,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,66861.75463867188,123,123
-28e582c7,float32,500,500,5,None,1.0,0.0519,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,8771.281860351562,123,123
-93625d55,float32,500,500,5,random,1.0,0.0507,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,5395.750244140625,123,123
-33f75d20,float32,500,500,1,None,1.0,0.0512,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,377.2563629150391,123,123
-f89a30fa,float32,500,500,1,random,1.0,0.0919,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,242.02552032470703,123,123
+561d0ce5,float32,1500,1500,20,random,1.0,0.477,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,295619.1181640625,123,123
+79df633d,float32,1500,1500,5,None,1.0,0.441,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,34620.72705078125,123,123
+9e51b4f6,float32,1500,1500,5,random,1.0,0.435,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,21469.46875,123,123
+63405b3d,float32,1500,1500,1,None,1.0,0.436,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,2250.33251953125,123,123
+784cd81e,float32,1500,1500,1,random,1.0,0.468,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,1258.864501953125,123,123

From ca43909885c984b34cdfe2a84e6c0a10c352b210 Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Fri, 12 Jan 2024 12:06:10 +0100
Subject: [PATCH 06/43] Order sheets alphabetically, ignore casing

---
 benchmarks/kmeans/consolidate_result_csv.py | 3 +--
 benchmarks/pca/consolidate_result_csv.py    | 3 +--
 benchmarks/ridge/consolidate_result_csv.py  | 4 +---
 3 files changed, 3 insertions(+), 7 deletions(-)

diff --git a/benchmarks/kmeans/consolidate_result_csv.py b/benchmarks/kmeans/consolidate_result_csv.py
index 00ef838..81e9a50 100644
--- a/benchmarks/kmeans/consolidate_result_csv.py
+++ b/benchmarks/kmeans/consolidate_result_csv.py
@@ -2,7 +2,6 @@
 from functools import partial
 from io import BytesIO
 from itertools import zip_longest
-from operator import attrgetter
 
 import numpy as np
 import pandas as pd
@@ -403,7 +402,7 @@ def _gspread_sync(source, gspread_url, gspread_auth_key):
         )
         # ensure worksheets are sorted anti-alphabetically
         sheet.reorder_worksheets(
-            sorted(sheet.worksheets(), key=attrgetter("title"), reverse=True)
+            sorted(sheet.worksheets(), key=lambda worksheet: worksheet.title.lower())
         )
 
     # upload all values
diff --git a/benchmarks/pca/consolidate_result_csv.py b/benchmarks/pca/consolidate_result_csv.py
index e4e1a3f..fc34b3f 100644
--- a/benchmarks/pca/consolidate_result_csv.py
+++ b/benchmarks/pca/consolidate_result_csv.py
@@ -2,7 +2,6 @@
 from functools import partial
 from io import BytesIO
 from itertools import zip_longest
-from operator import attrgetter
 
 import numpy as np
 import pandas as pd
@@ -401,7 +400,7 @@ def _gspread_sync(source, gspread_url, gspread_auth_key):
         )
         # ensure worksheets are sorted anti-alphabetically
         sheet.reorder_worksheets(
-            sorted(sheet.worksheets(), key=attrgetter("title"), reverse=True)
+            sorted(sheet.worksheets(), key=lambda worksheet: worksheet.title.lower())
         )
 
     # upload all values
diff --git a/benchmarks/ridge/consolidate_result_csv.py b/benchmarks/ridge/consolidate_result_csv.py
index fd5cc2f..a991669 100644
--- a/benchmarks/ridge/consolidate_result_csv.py
+++ b/benchmarks/ridge/consolidate_result_csv.py
@@ -407,9 +407,7 @@ def _gspread_sync(source, gspread_url, gspread_auth_key):
             GOOGLE_WORKSHEET_NAME, rows=n_rows + 1, cols=n_cols
         )
         # ensure worksheets are sorted anti-alphabetically
-        sheet.reorder_worksheets(
-            sorted(sheet.worksheets(), key=attrgetter("title"), reverse=True)
-        )
+        sheet.reorder_worksheets(sorted(sheet.worksheets(), key=attrgetter("title")))
 
     # upload all values
     worksheet.update(

From 2be4ee7b9683f73f3bdf6c070cc2f4f47cd085d3 Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Fri, 12 Jan 2024 12:08:02 +0100
Subject: [PATCH 07/43] do not display alpha column in the spreadsheet

---
 benchmarks/ridge/consolidate_result_csv.py | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/benchmarks/ridge/consolidate_result_csv.py b/benchmarks/ridge/consolidate_result_csv.py
index a991669..3b152d2 100644
--- a/benchmarks/ridge/consolidate_result_csv.py
+++ b/benchmarks/ridge/consolidate_result_csv.py
@@ -38,7 +38,6 @@
 DATA_RANDOM_STATE = "Data random state"
 DATA_SAMPLE_WEIGHTS = "Data sample weights"
 DTYPE = "Dtype"
-ALPHA = "alpha"
 NB_DATA_FEATURES = "Nb data features"
 NB_DATA_SAMPLES = "Nb data samples"
 NB_DATA_TARGETS = "Nb data targets"
@@ -68,7 +67,6 @@
     NB_DATA_FEATURES,
     NB_DATA_TARGETS,
     DATA_SAMPLE_WEIGHTS,
-    ALPHA,
     WALLTIME,
     BACKEND_PROVIDER,
     COMPUTE_DEVICE,
@@ -96,7 +94,6 @@
     NB_DATA_SAMPLES: np.int64,
     NB_DATA_FEATURES: np.int64,
     NB_DATA_TARGETS: np.int64,
-    ALPHA: np.float64,
     DATA_SAMPLE_WEIGHTS: str,
     WALLTIME: np.float64,
     BACKEND_PROVIDER: str,
@@ -150,7 +147,6 @@
     (NB_DATA_FEATURES, False),
     (NB_DATA_TARGETS, False),
     (DATA_SAMPLE_WEIGHTS, True),
-    (ALPHA, False),
     (WALLTIME, True),
     (BACKEND_PROVIDER, True),
     (COMPUTE_DEVICE, True),
@@ -183,7 +179,6 @@
     objective_dataset_param_n_targets=NB_DATA_TARGETS,
     objective_dataset_param_dtype=DTYPE,
     objective_dataset_param_random_state=DATA_RANDOM_STATE,
-    objective_objective_param_alpha=ALPHA,
     objective_objective_param_random_state=SOLVER_RANDOM_STATE,
     objective_objective_param_sample_weight=DATA_SAMPLE_WEIGHTS,
     objective_solver_param___name=BACKEND_PROVIDER,

From 04803544a1c63ba90d9720c6120e680343ca4c58 Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Fri, 12 Jan 2024 12:16:36 +0100
Subject: [PATCH 08/43] Fix solver column

---
 benchmarks/ridge/consolidate_result_csv.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/benchmarks/ridge/consolidate_result_csv.py b/benchmarks/ridge/consolidate_result_csv.py
index 3b152d2..4cc59d1 100644
--- a/benchmarks/ridge/consolidate_result_csv.py
+++ b/benchmarks/ridge/consolidate_result_csv.py
@@ -181,10 +181,10 @@
     objective_dataset_param_random_state=DATA_RANDOM_STATE,
     objective_objective_param_random_state=SOLVER_RANDOM_STATE,
     objective_objective_param_sample_weight=DATA_SAMPLE_WEIGHTS,
+    objective_objective_param_solver=SOLVER,
     objective_solver_param___name=BACKEND_PROVIDER,
     objective_solver_param_device=COMPUTE_DEVICE,
     objective_solver_param_runtime=COMPUTE_RUNTIME,
-    objective_solver_param_solver=SOLVER,
     objective_solver_param_comment=COMMENT,
     objective_solver_param_version_info=VERSION_INFO,
     objective_solver_param_run_date=RUN_DATE,

From 00005f02e446383b99dbebc2c16bdb2e3da778f2 Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Fri, 12 Jan 2024 13:25:03 +0100
Subject: [PATCH 09/43] move data copy and objective evaluation in objective
 dedicated methods

---
 benchmarks/ridge/objective.py            | 34 ++++++++++++++++++++----
 benchmarks/ridge/solvers/scikit_learn.py | 29 +++++---------------
 2 files changed, 35 insertions(+), 28 deletions(-)

diff --git a/benchmarks/ridge/objective.py b/benchmarks/ridge/objective.py
index 40b2713..9242ecc 100644
--- a/benchmarks/ridge/objective.py
+++ b/benchmarks/ridge/objective.py
@@ -4,6 +4,7 @@
 
 with safe_import_context() as import_ctx:
     import numpy as np
+    from sklearn.linear_model._base import _rescale_data
 
 
 class Objective(BaseObjective):
@@ -47,7 +48,21 @@ def set_data(self, X, y, **dataset_parameters):
         self.sample_weight_ = sample_weight
         self.dataset_parameters = dataset_parameters
 
-    def evaluate_result(self, objective, **solver_parameters):
+    def evaluate_result(self, weights, intercept, **solver_parameters):
+        # NB: weights, intercept expected to be numpy arrays
+
+        X, y = self.X, self.y
+        if self.sample_weight_ is not None:
+            X, y, _ = _rescale_data(X, y, self.sample_weight_, inplace=False)
+
+        y = y.reshape((y.shape[0], -1))
+        weights = weights.reshape((-1, X.shape[1], 1))
+
+        value = (
+            (((X @ weights).squeeze(2) + (intercept - y).T) ** 2).sum()
+            + (self.alpha * (weights**2).sum())
+        ) / (X.shape[0] * len(y.T))
+
         all_parameters = dict(solver_param_run_date=datetime.today())
         all_parameters.update(
             {
@@ -65,7 +80,7 @@ def evaluate_result(self, objective, **solver_parameters):
             {("solver_param_" + key): value for key, value in solver_parameters.items()}
         )
         return dict(
-            value=objective,
+            value=value,
             objective_param___name=self.name,
             **all_parameters,
         )
@@ -74,10 +89,19 @@ def get_one_result(self):
         return dict(objective=1)
 
     def get_objective(self):
+        # Copy the data before sending to the solver, to ensure that no unfortunate
+        # side effects can happen
+        X = self.X.copy()
+        y = self.y.copy()
+
+        sample_weight = self.sample_weight_
+        if hasattr(sample_weight, "copy"):
+            sample_weight = sample_weight.copy()
+
         return dict(
-            X=self.X,
-            y=self.y,
-            sample_weight=self.sample_weight_,
+            X=X,
+            y=y,
+            sample_weight=sample_weight,
             alpha=self.alpha,
             fit_intercept=self.fit_intercept,
             solver=self.solver,
diff --git a/benchmarks/ridge/solvers/scikit_learn.py b/benchmarks/ridge/solvers/scikit_learn.py
index dec54da..4b5c4eb 100644
--- a/benchmarks/ridge/solvers/scikit_learn.py
+++ b/benchmarks/ridge/solvers/scikit_learn.py
@@ -5,7 +5,6 @@
 
 with safe_import_context() as import_ctx:
     from sklearn.linear_model import Ridge
-    from sklearn.linear_model._base import _rescale_data
 
 
 class Solver(BaseSolver):
@@ -26,15 +25,9 @@ def set_objective(
         tol,
         random_state,
     ):
-        # Copy the data before running the benchmark to ensure that no unfortunate side
-        # effects can happen
-        self.X = X.copy()
-        self.y = y.copy()
-
-        if hasattr(sample_weight, "copy"):
-            sample_weight = sample_weight.copy()
+        self.X = X
+        self.y = y
         self.sample_weight = sample_weight
-
         self.alpha = alpha
         self.fit_intercept = fit_intercept
         self.solver = solver
@@ -54,23 +47,13 @@ def run(self, _):
             random_state=self.random_state,
         ).fit(self.X, self.y, self.sample_weight)
 
-        self.coef_ = estimator.coef_
+        self.weights = estimator.coef_
+        self.intercept = estimator.intercept_
 
     def get_result(self):
-        X, y = self.X, self.y
-        if self.sample_weight is not None:
-            X, y, _ = _rescale_data(X, y, self.sample_weight, inplace=True)
-
-        y = y.reshape((y.shape[0], -1))
-
-        coef_ = self.coef_.reshape((-1, X.shape[1], 1))
-
-        objective = ((y.T - (X @ coef_).squeeze(2)) ** 2).sum() + (
-            len(y.T) * self.alpha * (coef_**2).sum()
-        )
-
         return dict(
-            objective=objective,
+            weights=self.weights,
+            intercept=self.intercept,
             version_info=f"scikit-learn {version('scikit-learn')}",
             __name=self.name,
             **self._parameters,

From 0bcb8908c542feb006e3f59722127b292c6c0df8 Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Fri, 12 Jan 2024 14:09:35 +0100
Subject: [PATCH 10/43] Update results.csv

---
 benchmarks/ridge/results.csv | 62 ++++++++++++++++++------------------
 1 file changed, 31 insertions(+), 31 deletions(-)

diff --git a/benchmarks/ridge/results.csv b/benchmarks/ridge/results.csv
index b849b5e..e2c2e4c 100644
--- a/benchmarks/ridge/results.csv
+++ b/benchmarks/ridge/results.csv
@@ -1,31 +1,31 @@
-Benchmark id,Dtype,Nb data samples,Nb data features,Nb data targets,Data sample weights,alpha,Walltime,Backend provider,Compute device,Compute runtime,Solver,Nb cpus,Cpu name,Gpu name,RAM (GB),Platform,Platform architecture,Platform release,Run date,Version info,Comment,Result objective value,Data random state,Solver random state
-a78d35ba,float32,20000000,100,20,None,1.0,56,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,922550919.371338,123,123
-8b1100fe,float32,20000000,100,20,random,1.0,59.6,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,461288551.440918,123,123
-17b28ecb,float32,20000000,100,5,None,1.0,55.5,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,129293185.74858856,123,123
-1860575f,float32,20000000,100,5,random,1.0,58.4,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,64640129.75015259,123,123
-2e28ac06,float32,20000000,100,1,None,1.0,54.7,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,28933007.926410675,123,123
-c8f238fc,float32,20000000,100,1,random,1.0,60.8,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,14463809.926801682,123,123
-f7f7d3ec,float32,20000000,10,20,None,1.0,4.3,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,99911837.59606934,123,123
-d73672fb,float32,20000000,10,20,random,1.0,5.46,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,49956789.596069336,123,123
-32dcde45,float32,20000000,10,5,None,1.0,3.81,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,23062447.030111317,123,123
-ab137d0c,float32,20000000,10,5,random,1.0,4.39,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,11530631.03193283,123,123
-811a6242,float32,20000000,10,1,None,1.0,2.9,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,8171019.225429058,123,123
-f3eb07b0,float32,20000000,10,1,random,1.0,3.38,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,4085583.225161791,123,123
-aadcc062,float32,2000000,100,20,None,1.0,6.8,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,93357177.85253906,123,123
-b94007e8,float32,2000000,100,20,random,1.0,7.24,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,46702286.408569336,123,123
-95cf6cca,float32,2000000,100,5,None,1.0,5.8,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,20061771.567626957,123,123
-573fa149,float32,2000000,100,5,random,1.0,6.1,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,10032954.551605225,123,123
-32973908,float32,2000000,100,1,None,1.0,5.54,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,4566305.682353973,123,123
-91a6dbe0,float32,2000000,100,1,random,1.0,7.53,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,2284366.68109703,123,123
-fa82693a,float32,5000,5000,20,None,1.0,7.65,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,2797818.703125,123,123
-d5d61723,float32,5000,5000,20,random,1.0,7.73,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,1539654.8515625,123,123
-cb92ea74,float32,5000,5000,5,None,1.0,7.73,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,199817.18359375,123,123
-5c90e258,float32,5000,5000,5,random,1.0,7.73,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,109143.365234375,123,123
-62f48fdb,float32,5000,5000,1,None,1.0,7.66,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,11393.48388671875,123,123
-6d09b315,float32,5000,5000,1,random,1.0,7.77,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,5781.953369140625,123,123
-46bef6c6,float32,1500,1500,20,None,1.0,0.426,scikit-learn,,,,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,475352.138671875,123,123
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+Benchmark id,Dtype,Nb data samples,Nb data features,Nb data targets,Data sample weights,Walltime,Backend provider,Compute device,Compute runtime,Solver,Nb cpus,Cpu name,Gpu name,RAM (GB),Platform,Platform architecture,Platform release,Run date,Version info,Comment,Result objective value,Data random state,Solver random state
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+8b1100fe,float32,20000000,100,20,random,58.9,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,1.1532024249301147,123,123
+17b28ecb,float32,20000000,100,5,None,54.9,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,1.292928387497177,123,123
+1860575f,float32,20000000,100,5,random,57.7,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,0.6463988675003052,123,123
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+c8f238fc,float32,20000000,100,1,random,60.5,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,0.723188996340084,123,123
+f7f7d3ec,float32,20000000,10,20,None,4.53,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,0.2497772326995086,123,123
+d73672fb,float32,20000000,10,20,random,5.38,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,0.1248897226995086,123,123
+32dcde45,float32,20000000,10,5,None,3.66,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,0.2306240220602226,123,123
+ab137d0c,float32,20000000,10,5,random,4.38,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,0.1153058720638656,123,123
+811a6242,float32,20000000,10,1,None,2.93,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,0.4085507112714529,123,123
+f3eb07b0,float32,20000000,10,1,random,3.45,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,0.2042790112580895,123,123
+aadcc062,float32,2000000,100,20,None,5.84,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,2.3337219423156736,123,123
+b94007e8,float32,2000000,100,20,random,6.96,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,1.1673499430107117,123,123
+95cf6cca,float32,2000000,100,5,None,5.77,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,2.006134111352539,123,123
+573fa149,float32,2000000,100,5,random,6.3,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,1.0032485110321046,123,123
+32973908,float32,2000000,100,1,None,5.41,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,2.283151841176987,123,123
+91a6dbe0,float32,2000000,100,1,random,7.02,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,1.1421833405485151,123,123
+fa82693a,float32,5000,5000,20,None,7.58,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,2.46500453125,123,123
+d5d61723,float32,5000,5000,20,random,7.7,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,1.2509208203125,123,123
+cb92ea74,float32,5000,5000,5,None,7.59,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,2.539740703125,123,123
+5c90e258,float32,5000,5000,5,random,7.63,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,1.2494446484375,123,123
+62f48fdb,float32,5000,5000,1,None,7.55,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,2.20544296875,123,123
+6d09b315,float32,5000,5000,1,random,7.59,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,1.164737255859375,123,123
+46bef6c6,float32,1500,1500,20,None,0.44,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,1.2916935872395834,123,123
+561d0ce5,float32,1500,1500,20,random,0.443,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,0.750314453125,123,123
+79df633d,float32,1500,1500,5,None,0.425,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,1.4401695963541667,123,123
+9e51b4f6,float32,1500,1500,5,random,0.46,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,0.7841548177083333,123,123
+63405b3d,float32,1500,1500,1,None,0.424,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,1.2072095540364582,123,123
+784cd81e,float32,1500,1500,1,random,0.479,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,0.6977200317382812,123,123

From 82b3ec5176de844f29a5df730fa0434e3ea3f2f8 Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Fri, 12 Jan 2024 14:27:21 +0100
Subject: [PATCH 11/43] update yaml

---
 .github/workflows/sync_benchmark_files_to_gsheet.yaml | 4 ++--
 .github/workflows/test_cpu_benchmarks.yaml            | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/sync_benchmark_files_to_gsheet.yaml b/.github/workflows/sync_benchmark_files_to_gsheet.yaml
index 24505a6..c99700b 100644
--- a/.github/workflows/sync_benchmark_files_to_gsheet.yaml
+++ b/.github/workflows/sync_benchmark_files_to_gsheet.yaml
@@ -24,11 +24,11 @@ jobs:
       run: |
         python ./benchmarks/kmeans/consolidate_result_csv.py ./benchmarks/kmeans/results.csv --check-csv
         python ./benchmarks/pca/consolidate_result_csv.py ./benchmarks/pca/results.csv --check-csv
-        python ./benchmarks/ridge/consolidate_result_csv.py ./benchmarks/pca/results.csv --check-csv
+        python ./benchmarks/ridge/consolidate_result_csv.py ./benchmarks/ridge/results.csv --check-csv
         echo "$GSPREAD_SERVICE_ACCOUNT_AUTH_KEY" > service_account.json
         python ./benchmarks/kmeans/consolidate_result_csv.py ./benchmarks/kmeans/results.csv \
           --sync-to-gspread --gspread-url $GSPREAD_URL --gspread-auth-key ./service_account.json
         python ./benchmarks/pca/consolidate_result_csv.py ./benchmarks/pca/results.csv \
           --sync-to-gspread --gspread-url $GSPREAD_URL --gspread-auth-key ./service_account.json
-        python ./benchmarks/ridge/consolidate_result_csv.py ./benchmarks/pca/results.csv \
+        python ./benchmarks/ridge/consolidate_result_csv.py ./benchmarks/ridge/results.csv \
           --sync-to-gspread --gspread-url $GSPREAD_URL --gspread-auth-key ./service_account.json
diff --git a/.github/workflows/test_cpu_benchmarks.yaml b/.github/workflows/test_cpu_benchmarks.yaml
index 31cfda8..4bb5cc5 100644
--- a/.github/workflows/test_cpu_benchmarks.yaml
+++ b/.github/workflows/test_cpu_benchmarks.yaml
@@ -144,4 +144,4 @@ jobs:
         cd ../pca
         benchopt run --no-plot -l -d Simulated_correlated_data[n_samples=100,n_features=100]
         cd ../ridge
-        benchopt run --no-plot -l -d Simulated_correlated_data[n_samples=100,n_features=100]  # TODO add relevant parameters
+        benchopt run --no-plot -l -d Simulated_correlated_data[n_samples=100,n_features=100,n_targets=2]

From 0ce20138a74fabb499dd5053177a77eb25bbac93 Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Fri, 12 Jan 2024 19:45:22 +0100
Subject: [PATCH 12/43] Trigger ci after fix


From 6bb0aa520e176c271182a40488b67da82cce577a Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Fri, 12 Jan 2024 20:50:27 +0100
Subject: [PATCH 13/43] Adress review

Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>
---
 benchmarks/ridge/consolidate_result_csv.py   |  8 +-------
 benchmarks/ridge/datasets/simulated_blobs.py |  7 +++----
 benchmarks/ridge/objective.py                | 16 ++++++++++++++--
 3 files changed, 18 insertions(+), 13 deletions(-)

diff --git a/benchmarks/ridge/consolidate_result_csv.py b/benchmarks/ridge/consolidate_result_csv.py
index 4cc59d1..ddf5938 100644
--- a/benchmarks/ridge/consolidate_result_csv.py
+++ b/benchmarks/ridge/consolidate_result_csv.py
@@ -36,7 +36,6 @@
 COMPUTE_DEVICE = "Compute device"
 COMPUTE_RUNTIME = "Compute runtime"
 DATA_RANDOM_STATE = "Data random state"
-DATA_SAMPLE_WEIGHTS = "Data sample weights"
 DTYPE = "Dtype"
 NB_DATA_FEATURES = "Nb data features"
 NB_DATA_SAMPLES = "Nb data samples"
@@ -66,7 +65,6 @@
     NB_DATA_SAMPLES,
     NB_DATA_FEATURES,
     NB_DATA_TARGETS,
-    DATA_SAMPLE_WEIGHTS,
     WALLTIME,
     BACKEND_PROVIDER,
     COMPUTE_DEVICE,
@@ -94,7 +92,6 @@
     NB_DATA_SAMPLES: np.int64,
     NB_DATA_FEATURES: np.int64,
     NB_DATA_TARGETS: np.int64,
-    DATA_SAMPLE_WEIGHTS: str,
     WALLTIME: np.float64,
     BACKEND_PROVIDER: str,
     COMPUTE_DEVICE: str,
@@ -116,7 +113,7 @@
     COMMENT: str,
 }
 
-COLUMNS_WITH_NONE_STRING = [DATA_SAMPLE_WEIGHTS]
+COLUMNS_WITH_NONE_STRING = []
 
 # If all those fields have equal values for two given benchmarks, then the oldest
 # benchmark (given by RUN_DATE) will be discarded
@@ -126,7 +123,6 @@
     NB_DATA_SAMPLES,
     NB_DATA_FEATURES,
     NB_DATA_TARGETS,
-    DATA_SAMPLE_WEIGHTS,
     BACKEND_PROVIDER,
     SOLVER,
     COMPUTE_DEVICE,
@@ -146,7 +142,6 @@
     (NB_DATA_SAMPLES, False),
     (NB_DATA_FEATURES, False),
     (NB_DATA_TARGETS, False),
-    (DATA_SAMPLE_WEIGHTS, True),
     (WALLTIME, True),
     (BACKEND_PROVIDER, True),
     (COMPUTE_DEVICE, True),
@@ -180,7 +175,6 @@
     objective_dataset_param_dtype=DTYPE,
     objective_dataset_param_random_state=DATA_RANDOM_STATE,
     objective_objective_param_random_state=SOLVER_RANDOM_STATE,
-    objective_objective_param_sample_weight=DATA_SAMPLE_WEIGHTS,
     objective_objective_param_solver=SOLVER,
     objective_solver_param___name=BACKEND_PROVIDER,
     objective_solver_param_device=COMPUTE_DEVICE,
diff --git a/benchmarks/ridge/datasets/simulated_blobs.py b/benchmarks/ridge/datasets/simulated_blobs.py
index e8db00c..74daf4a 100644
--- a/benchmarks/ridge/datasets/simulated_blobs.py
+++ b/benchmarks/ridge/datasets/simulated_blobs.py
@@ -10,13 +10,12 @@ class Dataset(BaseDataset):
 
     parameters = {
         "n_samples, n_features": [
-            (20_000_000, 100),
+            (10_000_000, 100),
             (5000, 5000),
-            (20_000_000, 10),
+            (10_000_000, 10),
             (2_000_000, 100),
-            (1500, 1500),
         ],
-        "n_targets": [1, 5, 20],
+        "n_targets": [1, 10],
         "dtype": ["float32"],
         "random_state": [123],
     }
diff --git a/benchmarks/ridge/objective.py b/benchmarks/ridge/objective.py
index 9242ecc..1a7dc89 100644
--- a/benchmarks/ridge/objective.py
+++ b/benchmarks/ridge/objective.py
@@ -21,7 +21,7 @@ class Objective(BaseObjective):
         "alpha": [1.0],
         "fit_intercept": [True],
         "solver, max_iter, tol": [("svd", None, 0)],
-        "sample_weight": ["None", "random"],
+        "sample_weight": ["None"],  # NB: add "random" to test non None weights
         "random_state": [123],
     }
 
@@ -86,7 +86,19 @@ def evaluate_result(self, weights, intercept, **solver_parameters):
         )
 
     def get_one_result(self):
-        return dict(objective=1)
+        n_features = self.dataset_parameters["n_features"]
+        n_targets = self.dataset_parameters["n_targets"]
+        if n_targets == 1:
+            weights = np.ones((n_features,))
+        else:
+            weights = np.ones(
+                (
+                    n_targets,
+                    n_features,
+                )
+            )
+
+        return dict(weights=weights, intercept=np.ones((n_targets,)))
 
     def get_objective(self):
         # Copy the data before sending to the solver, to ensure that no unfortunate

From 5fddbac562c376ee4fba3582b6bbc458db25801f Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Fri, 12 Jan 2024 21:11:21 +0100
Subject: [PATCH 14/43] Tune down dataset size and update results

---
 benchmarks/ridge/datasets/simulated_blobs.py  |   4 +-
 .../benchopt_run_2024-01-12_11h52m30.parquet  | Bin 32880 -> 0 bytes
 .../benchopt_run_2024-01-12_21h05m55.parquet  | Bin 0 -> 32152 bytes
 benchmarks/ridge/results.csv                  |  40 ++++--------------
 4 files changed, 11 insertions(+), 33 deletions(-)
 delete mode 100644 benchmarks/ridge/outputs/benchopt_run_2024-01-12_11h52m30.parquet
 create mode 100644 benchmarks/ridge/outputs/benchopt_run_2024-01-12_21h05m55.parquet

diff --git a/benchmarks/ridge/datasets/simulated_blobs.py b/benchmarks/ridge/datasets/simulated_blobs.py
index 74daf4a..68e40f4 100644
--- a/benchmarks/ridge/datasets/simulated_blobs.py
+++ b/benchmarks/ridge/datasets/simulated_blobs.py
@@ -10,9 +10,9 @@ class Dataset(BaseDataset):
 
     parameters = {
         "n_samples, n_features": [
-            (10_000_000, 100),
+            (5_000_000, 100),
             (5000, 5000),
-            (10_000_000, 10),
+            (5_000_000, 10),
             (2_000_000, 100),
         ],
         "n_targets": [1, 10],
diff --git a/benchmarks/ridge/outputs/benchopt_run_2024-01-12_11h52m30.parquet b/benchmarks/ridge/outputs/benchopt_run_2024-01-12_11h52m30.parquet
deleted file mode 100644
index 4d45266494158aaeb442d33412c98af44a8fc6af..0000000000000000000000000000000000000000
GIT binary patch
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diff --git a/benchmarks/ridge/results.csv b/benchmarks/ridge/results.csv
index e2c2e4c..bf44331 100644
--- a/benchmarks/ridge/results.csv
+++ b/benchmarks/ridge/results.csv
@@ -1,31 +1,9 @@
-Benchmark id,Dtype,Nb data samples,Nb data features,Nb data targets,Data sample weights,Walltime,Backend provider,Compute device,Compute runtime,Solver,Nb cpus,Cpu name,Gpu name,RAM (GB),Platform,Platform architecture,Platform release,Run date,Version info,Comment,Result objective value,Data random state,Solver random state
-a78d35ba,float32,20000000,100,20,None,55.8,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,2.306357464921417,123,123
-8b1100fe,float32,20000000,100,20,random,58.9,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,1.1532024249301147,123,123
-17b28ecb,float32,20000000,100,5,None,54.9,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,1.292928387497177,123,123
-1860575f,float32,20000000,100,5,random,57.7,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,0.6463988675003052,123,123
-2e28ac06,float32,20000000,100,1,None,53.9,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,1.4466487963205337,123,123
-c8f238fc,float32,20000000,100,1,random,60.5,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,0.723188996340084,123,123
-f7f7d3ec,float32,20000000,10,20,None,4.53,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,0.2497772326995086,123,123
-d73672fb,float32,20000000,10,20,random,5.38,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,0.1248897226995086,123,123
-32dcde45,float32,20000000,10,5,None,3.66,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,0.2306240220602226,123,123
-ab137d0c,float32,20000000,10,5,random,4.38,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,0.1153058720638656,123,123
-811a6242,float32,20000000,10,1,None,2.93,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,0.4085507112714529,123,123
-f3eb07b0,float32,20000000,10,1,random,3.45,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,0.2042790112580895,123,123
-aadcc062,float32,2000000,100,20,None,5.84,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,2.3337219423156736,123,123
-b94007e8,float32,2000000,100,20,random,6.96,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,1.1673499430107117,123,123
-95cf6cca,float32,2000000,100,5,None,5.77,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,2.006134111352539,123,123
-573fa149,float32,2000000,100,5,random,6.3,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,1.0032485110321046,123,123
-32973908,float32,2000000,100,1,None,5.41,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,2.283151841176987,123,123
-91a6dbe0,float32,2000000,100,1,random,7.02,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,1.1421833405485151,123,123
-fa82693a,float32,5000,5000,20,None,7.58,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,2.46500453125,123,123
-d5d61723,float32,5000,5000,20,random,7.7,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,1.2509208203125,123,123
-cb92ea74,float32,5000,5000,5,None,7.59,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,2.539740703125,123,123
-5c90e258,float32,5000,5000,5,random,7.63,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,1.2494446484375,123,123
-62f48fdb,float32,5000,5000,1,None,7.55,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,2.20544296875,123,123
-6d09b315,float32,5000,5000,1,random,7.59,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,1.164737255859375,123,123
-46bef6c6,float32,1500,1500,20,None,0.44,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,1.2916935872395834,123,123
-561d0ce5,float32,1500,1500,20,random,0.443,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,0.750314453125,123,123
-79df633d,float32,1500,1500,5,None,0.425,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,1.4401695963541667,123,123
-9e51b4f6,float32,1500,1500,5,random,0.46,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,0.7841548177083333,123,123
-63405b3d,float32,1500,1500,1,None,0.424,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,1.2072095540364582,123,123
-784cd81e,float32,1500,1500,1,random,0.479,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,0.6977200317382812,123,123
+Benchmark id,Dtype,Nb data samples,Nb data features,Nb data targets,Walltime,Backend provider,Compute device,Compute runtime,Solver,Nb cpus,Cpu name,Gpu name,RAM (GB),Platform,Platform architecture,Platform release,Run date,Version info,Comment,Result objective value,Data random state,Solver random state
+a1699bfa,float32,5000000,100,10,17.8,scikit-learn,,,svd,32,Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz,,377,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,2.0798046131018064,123,123
+c936df3a,float32,5000000,100,1,18,scikit-learn,,,svd,32,Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz,,377,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,4.506280786405563,123,123
+e31dbef7,float32,5000000,10,10,1.28,scikit-learn,,,svd,32,Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz,,377,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,0.1351635753305053,123,123
+6f023980,float32,5000000,10,1,0.959,scikit-learn,,,svd,32,Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz,,377,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,0.025126947566992,123,123
+a4cf3e5b,float32,2000000,100,10,7,scikit-learn,,,svd,32,Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz,,377,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,2.1605555927993776,123,123
+32973908,float32,2000000,100,1,6.63,scikit-learn,,,svd,32,Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz,,377,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,2.283151841173172,123,123
+b64c1a14,float32,5000,5000,10,9.79,scikit-learn,,,svd,32,Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz,,377,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,2.375214453125,123,123
+62f48fdb,float32,5000,5000,1,9.95,scikit-learn,,,svd,32,Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz,,377,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,2.20544287109375,123,123

From ac5a5c9d77a5136397ce5a6233e1d3b92ab77378 Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Mon, 15 Jan 2024 10:22:21 +0100
Subject: [PATCH 15/43] Add other scikit-learn solvers

---
 benchmarks/ridge/objective.py            | 10 +++++++++-
 benchmarks/ridge/solvers/scikit_learn.py | 17 +++++++++++++++++
 2 files changed, 26 insertions(+), 1 deletion(-)

diff --git a/benchmarks/ridge/objective.py b/benchmarks/ridge/objective.py
index 1a7dc89..98056c7 100644
--- a/benchmarks/ridge/objective.py
+++ b/benchmarks/ridge/objective.py
@@ -20,7 +20,15 @@ class Objective(BaseObjective):
     parameters = {
         "alpha": [1.0],
         "fit_intercept": [True],
-        "solver, max_iter, tol": [("svd", None, 0)],
+        "solver, max_iter, tol": [
+            ("svd", None, 0),
+            ("cholesky", None, 0),
+            ("lsqr", 25, 0),
+            ("sparse_cg", 25, 0),
+            ("sag", 50, 0),
+            ("saga", 25, 0),
+            ("lbfgs", 100, 0),
+        ],
         "sample_weight": ["None"],  # NB: add "random" to test non None weights
         "random_state": [123],
     }
diff --git a/benchmarks/ridge/solvers/scikit_learn.py b/benchmarks/ridge/solvers/scikit_learn.py
index 4b5c4eb..8ebef71 100644
--- a/benchmarks/ridge/solvers/scikit_learn.py
+++ b/benchmarks/ridge/solvers/scikit_learn.py
@@ -35,6 +35,23 @@ def set_objective(
         self.tol = tol
         self.random_state = random_state
 
+    def skip(self, **objective_dict):
+        y = objective_dict["y"]
+        solver = objective_dict["solver"]
+
+        if solver == "sparse_cg":
+            # TODO: investigate ? seems to freeze or take very long time
+            return True, "sparse_cg doesn't seem to work well at the moment ?"
+
+        is_multitarget = (y.ndim == 2) and (y.shape[1] > 1)
+
+        if is_multitarget and solver.startswith("sag"):
+            # TODO: investigate ? it freezes or take very long time
+            # at most should multiply single-target time by number of targets.
+            return True, "sag and saga doesn't support well multitarget."
+
+        return False, None
+
     def run(self, _):
         estimator = Ridge(
             alpha=self.alpha,

From 50a2cd5158f370d8a7ea9ee74a8572539a50a498 Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Mon, 15 Jan 2024 16:09:12 +0100
Subject: [PATCH 16/43] Skip sag, saga, sparse_cg and lbfgs ~ much too slow ?

---
 benchmarks/ridge/solvers/scikit_learn.py | 19 ++++++++-----------
 1 file changed, 8 insertions(+), 11 deletions(-)

diff --git a/benchmarks/ridge/solvers/scikit_learn.py b/benchmarks/ridge/solvers/scikit_learn.py
index 8ebef71..0d49ac0 100644
--- a/benchmarks/ridge/solvers/scikit_learn.py
+++ b/benchmarks/ridge/solvers/scikit_learn.py
@@ -36,19 +36,14 @@ def set_objective(
         self.random_state = random_state
 
     def skip(self, **objective_dict):
-        y = objective_dict["y"]
         solver = objective_dict["solver"]
 
-        if solver == "sparse_cg":
-            # TODO: investigate ? seems to freeze or take very long time
-            return True, "sparse_cg doesn't seem to work well at the moment ?"
-
-        is_multitarget = (y.ndim == 2) and (y.shape[1] > 1)
-
-        if is_multitarget and solver.startswith("sag"):
-            # TODO: investigate ? it freezes or take very long time
-            # at most should multiply single-target time by number of targets.
-            return True, "sag and saga doesn't support well multitarget."
+        if solver in ["sag", "saga", "sparse_cg", "lbfgs"]:
+            # TODO: investigate ?
+            return True, (
+                "Preliminary testing show this solver is too slow to have relevance "
+                "in the benchmark."
+            )
 
         return False, None
 
@@ -66,11 +61,13 @@ def run(self, _):
 
         self.weights = estimator.coef_
         self.intercept = estimator.intercept_
+        self.n_iter_ = estimator.n_iter_
 
     def get_result(self):
         return dict(
             weights=self.weights,
             intercept=self.intercept,
+            n_iter=self.n_iter_,
             version_info=f"scikit-learn {version('scikit-learn')}",
             __name=self.name,
             **self._parameters,

From ca4077f32c8c767b9f580c609ef9fef65c05fe49 Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Mon, 15 Jan 2024 16:13:29 +0100
Subject: [PATCH 17/43] Add number of iterations

---
 benchmarks/ridge/consolidate_result_csv.py | 12 +++++-------
 benchmarks/ridge/objective.py              |  3 ++-
 2 files changed, 7 insertions(+), 8 deletions(-)

diff --git a/benchmarks/ridge/consolidate_result_csv.py b/benchmarks/ridge/consolidate_result_csv.py
index ddf5938..25b2cf2 100644
--- a/benchmarks/ridge/consolidate_result_csv.py
+++ b/benchmarks/ridge/consolidate_result_csv.py
@@ -48,9 +48,7 @@
 SYSTEM_PROCESSOR = "Cpu name"
 SYSTEM_RAM = "RAM (GB)"
 SYSTEM_GPU = "Gpu name"
-# TODO: add number of iterations once iteration-based solvers are
-# added to the benchmark.
-# RESULT_NB_ITERATIONS = "Result nb iterations"
+RESULT_NB_ITERATIONS = "Result nb iterations"
 OBJECTIVE_FUNCTION_VALUE = "Result objective value"
 VERSION_INFO = "Version info"
 RUN_DATE = "Run date"
@@ -80,7 +78,7 @@
     RUN_DATE,
     VERSION_INFO,
     COMMENT,
-    # RESULT_NB_ITERATIONS,
+    RESULT_NB_ITERATIONS,
     OBJECTIVE_FUNCTION_VALUE,
     DATA_RANDOM_STATE,
     SOLVER_RANDOM_STATE,
@@ -96,7 +94,7 @@
     BACKEND_PROVIDER: str,
     COMPUTE_DEVICE: str,
     COMPUTE_RUNTIME: str,
-    # RESULT_NB_ITERATIONS: np.int64,
+    RESULT_NB_ITERATIONS: np.int64,
     OBJECTIVE_FUNCTION_VALUE: np.float64,
     SOLVER: str,
     PLATFORM: str,
@@ -146,7 +144,7 @@
     (BACKEND_PROVIDER, True),
     (COMPUTE_DEVICE, True),
     (COMPUTE_RUNTIME, True),
-    # (RESULT_NB_ITERATIONS, True),
+    (RESULT_NB_ITERATIONS, True),
     (OBJECTIVE_FUNCTION_VALUE, False),
     (SOLVER, True),
     (SYSTEM_GPU, True),
@@ -168,7 +166,7 @@
 PARQUET_TABLE_DISPLAY_MAPPING = dict(
     time=WALLTIME,
     objective_value=OBJECTIVE_FUNCTION_VALUE,
-    # objective_n_iter=RESULT_NB_ITERATIONS,
+    objective_n_iter=RESULT_NB_ITERATIONS,
     objective_dataset_param_n_samples=NB_DATA_SAMPLES,
     objective_dataset_param_n_features=NB_DATA_FEATURES,
     objective_dataset_param_n_targets=NB_DATA_TARGETS,
diff --git a/benchmarks/ridge/objective.py b/benchmarks/ridge/objective.py
index 98056c7..94c241c 100644
--- a/benchmarks/ridge/objective.py
+++ b/benchmarks/ridge/objective.py
@@ -56,7 +56,7 @@ def set_data(self, X, y, **dataset_parameters):
         self.sample_weight_ = sample_weight
         self.dataset_parameters = dataset_parameters
 
-    def evaluate_result(self, weights, intercept, **solver_parameters):
+    def evaluate_result(self, weights, intercept, n_iter, **solver_parameters):
         # NB: weights, intercept expected to be numpy arrays
 
         X, y = self.X, self.y
@@ -89,6 +89,7 @@ def evaluate_result(self, weights, intercept, **solver_parameters):
         )
         return dict(
             value=value,
+            n_iter=n_iter,
             objective_param___name=self.name,
             **all_parameters,
         )

From 2f9d3690b302f484b52d047683f57374ed4c6092 Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Mon, 15 Jan 2024 16:33:56 +0100
Subject: [PATCH 18/43] fix n_iter report

---
 benchmarks/ridge/solvers/scikit_learn.py | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/benchmarks/ridge/solvers/scikit_learn.py b/benchmarks/ridge/solvers/scikit_learn.py
index 0d49ac0..202e720 100644
--- a/benchmarks/ridge/solvers/scikit_learn.py
+++ b/benchmarks/ridge/solvers/scikit_learn.py
@@ -64,10 +64,20 @@ def run(self, _):
         self.n_iter_ = estimator.n_iter_
 
     def get_result(self):
+        n_iter = self.n_iter_
+        if isinstance(n_iter, list):
+            n_iter = set(n_iter)
+            if len(n_iter) > 1:
+                raise ValueError(
+                    "In multitarget mode, the same number of iterations is expected "
+                    "for all targets, to keep reports comparable."
+                )
+            n_iter = n_iter.pop()
+
         return dict(
             weights=self.weights,
             intercept=self.intercept,
-            n_iter=self.n_iter_,
+            n_iter=n_iter,
             version_info=f"scikit-learn {version('scikit-learn')}",
             __name=self.name,
             **self._parameters,

From 1cf65692f18ff0e8a5ccc59e5d765517a0bcf928 Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Mon, 15 Jan 2024 18:06:32 +0100
Subject: [PATCH 19/43] fix n_iter report

---
 benchmarks/ridge/consolidate_result_csv.py |  4 +++-
 benchmarks/ridge/objective.py              | 18 ++++++++++++++++++
 benchmarks/ridge/solvers/scikit_learn.py   | 12 +-----------
 3 files changed, 22 insertions(+), 12 deletions(-)

diff --git a/benchmarks/ridge/consolidate_result_csv.py b/benchmarks/ridge/consolidate_result_csv.py
index 25b2cf2..2933f92 100644
--- a/benchmarks/ridge/consolidate_result_csv.py
+++ b/benchmarks/ridge/consolidate_result_csv.py
@@ -94,7 +94,9 @@
     BACKEND_PROVIDER: str,
     COMPUTE_DEVICE: str,
     COMPUTE_RUNTIME: str,
-    RESULT_NB_ITERATIONS: np.int64,
+    # NB: following should be int but str is more practical because it enables
+    # use of missing values for solver for which it doesn't apply.
+    RESULT_NB_ITERATIONS: str,
     OBJECTIVE_FUNCTION_VALUE: np.float64,
     SOLVER: str,
     PLATFORM: str,
diff --git a/benchmarks/ridge/objective.py b/benchmarks/ridge/objective.py
index 94c241c..970fb21 100644
--- a/benchmarks/ridge/objective.py
+++ b/benchmarks/ridge/objective.py
@@ -1,3 +1,4 @@
+import numbers
 from datetime import datetime
 
 from benchopt import BaseObjective, safe_import_context
@@ -87,6 +88,23 @@ def evaluate_result(self, weights, intercept, n_iter, **solver_parameters):
         all_parameters.update(
             {("solver_param_" + key): value for key, value in solver_parameters.items()}
         )
+
+        if not (isinstance(n_iter, numbers.Number) or (n_iter is None)):
+            n_iter = set(n_iter)
+            if len(n_iter) > 1:
+                raise ValueError(
+                    "In multitarget mode, the same number of iterations is expected "
+                    "for all targets, to keep reports comparable."
+                )
+            n_iter = n_iter.pop()
+
+        # NB: str for n_iter is a more practical type because it enables
+        # using missing values for solvers for which it doesn't apply
+        if n_iter is None:
+            n_iter = ""
+        else:
+            n_iter = str(n_iter)
+
         return dict(
             value=value,
             n_iter=n_iter,
diff --git a/benchmarks/ridge/solvers/scikit_learn.py b/benchmarks/ridge/solvers/scikit_learn.py
index 202e720..0d49ac0 100644
--- a/benchmarks/ridge/solvers/scikit_learn.py
+++ b/benchmarks/ridge/solvers/scikit_learn.py
@@ -64,20 +64,10 @@ def run(self, _):
         self.n_iter_ = estimator.n_iter_
 
     def get_result(self):
-        n_iter = self.n_iter_
-        if isinstance(n_iter, list):
-            n_iter = set(n_iter)
-            if len(n_iter) > 1:
-                raise ValueError(
-                    "In multitarget mode, the same number of iterations is expected "
-                    "for all targets, to keep reports comparable."
-                )
-            n_iter = n_iter.pop()
-
         return dict(
             weights=self.weights,
             intercept=self.intercept,
-            n_iter=n_iter,
+            n_iter=self.n_iter_,
             version_info=f"scikit-learn {version('scikit-learn')}",
             __name=self.name,
             **self._parameters,

From 40fb09773092fe7e534e014c9447fa5cb633ee23 Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Mon, 15 Jan 2024 18:23:06 +0100
Subject: [PATCH 20/43] max_iter and tol out of benchmark unicity key ~ enable
 comparing solvers of different nature ?

---
 benchmarks/ridge/consolidate_result_csv.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/benchmarks/ridge/consolidate_result_csv.py b/benchmarks/ridge/consolidate_result_csv.py
index 2933f92..c578289 100644
--- a/benchmarks/ridge/consolidate_result_csv.py
+++ b/benchmarks/ridge/consolidate_result_csv.py
@@ -22,8 +22,6 @@
     "objective_dataset_param_random_state",
     "objective_objective_param_alpha",
     "objective_objective_param_fit_intercept",
-    "objective_objective_param_max_iter",
-    "objective_objective_param_tol",
     "objective_objective_param_sample_weight",
     "objective_objective_param_random_state",
 ]

From 60556a8a93ed4e930baf8cd45ead87e99d6ef379 Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Mon, 15 Jan 2024 18:24:22 +0100
Subject: [PATCH 21/43] update results sheet

---
 benchmarks/ridge/consolidate_result_csv.py    |   7 ++--
 .../benchopt_run_2024-01-12_21h05m55.parquet  | Bin 32152 -> 0 bytes
 .../benchopt_run_2024-01-15_18h08m05.parquet  | Bin 0 -> 33467 bytes
 benchmarks/ridge/results.csv                  |  34 +++++++++++++-----
 4 files changed, 30 insertions(+), 11 deletions(-)
 delete mode 100644 benchmarks/ridge/outputs/benchopt_run_2024-01-12_21h05m55.parquet
 create mode 100644 benchmarks/ridge/outputs/benchopt_run_2024-01-15_18h08m05.parquet

diff --git a/benchmarks/ridge/consolidate_result_csv.py b/benchmarks/ridge/consolidate_result_csv.py
index c578289..0461d71 100644
--- a/benchmarks/ridge/consolidate_result_csv.py
+++ b/benchmarks/ridge/consolidate_result_csv.py
@@ -139,7 +139,7 @@
     (DTYPE, True),
     (NB_DATA_SAMPLES, False),
     (NB_DATA_FEATURES, False),
-    (NB_DATA_TARGETS, False),
+    (NB_DATA_TARGETS, True),
     (WALLTIME, True),
     (BACKEND_PROVIDER, True),
     (COMPUTE_DEVICE, True),
@@ -393,8 +393,11 @@ def _gspread_sync(source, gspread_url, gspread_auth_key):
         worksheet = sheet.add_worksheet(
             GOOGLE_WORKSHEET_NAME, rows=n_rows + 1, cols=n_cols
         )
+
         # ensure worksheets are sorted anti-alphabetically
-        sheet.reorder_worksheets(sorted(sheet.worksheets(), key=attrgetter("title")))
+        sheet.reorder_worksheets(
+            sorted(sheet.worksheets(), key=lambda worksheet: worksheet.title.lower())
+        )
 
     # upload all values
     worksheet.update(
diff --git a/benchmarks/ridge/outputs/benchopt_run_2024-01-12_21h05m55.parquet b/benchmarks/ridge/outputs/benchopt_run_2024-01-12_21h05m55.parquet
deleted file mode 100644
index 8a75507401f46d88fb8619091d8631417a06089f..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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diff --git a/benchmarks/ridge/results.csv b/benchmarks/ridge/results.csv
index bf44331..697cf0e 100644
--- a/benchmarks/ridge/results.csv
+++ b/benchmarks/ridge/results.csv
@@ -1,9 +1,25 @@
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-c936df3a,float32,5000000,100,1,18,scikit-learn,,,svd,32,Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz,,377,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,4.506280786405563,123,123
-e31dbef7,float32,5000000,10,10,1.28,scikit-learn,,,svd,32,Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz,,377,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,0.1351635753305053,123,123
-6f023980,float32,5000000,10,1,0.959,scikit-learn,,,svd,32,Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz,,377,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,0.025126947566992,123,123
-a4cf3e5b,float32,2000000,100,10,7,scikit-learn,,,svd,32,Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz,,377,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,2.1605555927993776,123,123
-32973908,float32,2000000,100,1,6.63,scikit-learn,,,svd,32,Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz,,377,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,2.283151841173172,123,123
-b64c1a14,float32,5000,5000,10,9.79,scikit-learn,,,svd,32,Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz,,377,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,2.375214453125,123,123
-62f48fdb,float32,5000,5000,1,9.95,scikit-learn,,,svd,32,Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz,,377,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-12,scikit-learn 1.3.2,,2.20544287109375,123,123
+Benchmark id,Dtype,Nb data samples,Nb data features,Nb data targets,Walltime,Backend provider,Compute device,Compute runtime,Solver,Nb cpus,Cpu name,Gpu name,RAM (GB),Platform,Platform architecture,Platform release,Run date,Version info,Comment,Result nb iterations,Result objective value,Data random state,Solver random state
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+3a327106,float32,5000000,100,1,4.88,scikit-learn,,,lsqr,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,4.506281186382675,123,123
+3a327106,float32,5000000,100,1,16.8,scikit-learn,,,svd,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,4.506280786407089,123,123
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+9bd2a256,float32,5000000,100,10,16.9,scikit-learn,,,svd,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.079804613102112,123,123
+9bd2a256,float32,5000000,100,10,43,scikit-learn,,,lsqr,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,2.0798041330996706,123,123
+a9a05f25,float32,5000000,10,1,0.205,scikit-learn,,,cholesky,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,0.025126947566834,123,123
+a9a05f25,float32,5000000,10,1,0.743,scikit-learn,,,svd,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,0.025126946004498,123,123
+a9a05f25,float32,5000000,10,1,0.849,scikit-learn,,,lsqr,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,0.025126947566998,123,123
+ffd32c2e,float32,5000000,10,10,0.437,scikit-learn,,,cholesky,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,0.1351635453291702,123,123
+ffd32c2e,float32,5000000,10,10,1.17,scikit-learn,,,svd,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,0.135163585330429,123,123
+ffd32c2e,float32,5000000,10,10,7.72,scikit-learn,,,lsqr,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,0.1351635853305244,123,123
+c49c3df8,float32,2000000,100,1,0.692,scikit-learn,,,cholesky,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.283151591079712,123,123
+c49c3df8,float32,2000000,100,1,1.92,scikit-learn,,,lsqr,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,2.2831518411445617,123,123
+c49c3df8,float32,2000000,100,1,6.06,scikit-learn,,,svd,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.283151841176987,123,123
+1a8c391a,float32,2000000,100,10,0.761,scikit-learn,,,cholesky,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.160555392800904,123,123
+1a8c391a,float32,2000000,100,10,6.41,scikit-learn,,,svd,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.160555392799377,123,123
+1a8c391a,float32,2000000,100,10,18.2,scikit-learn,,,lsqr,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,2.1605555927993776,123,123
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+e5684553,float32,5000,5000,1,0.573,scikit-learn,,,cholesky,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.20544375,123,123
+e5684553,float32,5000,5000,1,10.2,scikit-learn,,,svd,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.20544296875,123,123
+7b05f2bf,float32,5000,5000,10,0.509,scikit-learn,,,cholesky,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.375214765625,123,123
+7b05f2bf,float32,5000,5000,10,1.22,scikit-learn,,,lsqr,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,9.804442734375,123,123
+7b05f2bf,float32,5000,5000,10,10.2,scikit-learn,,,svd,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.37521484375,123,123

From c5cb0bde457b2bc4f713fe61ba3944e25c6a186c Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Mon, 15 Jan 2024 18:35:13 +0100
Subject: [PATCH 22/43] linting

---
 benchmarks/ridge/consolidate_result_csv.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/benchmarks/ridge/consolidate_result_csv.py b/benchmarks/ridge/consolidate_result_csv.py
index 0461d71..b2cb42f 100644
--- a/benchmarks/ridge/consolidate_result_csv.py
+++ b/benchmarks/ridge/consolidate_result_csv.py
@@ -2,7 +2,6 @@
 from functools import partial
 from io import BytesIO
 from itertools import zip_longest
-from operator import attrgetter
 
 import numpy as np
 import pandas as pd

From 658bc8209ecc3d71812c59ef978c0681c25efc94 Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Mon, 15 Jan 2024 18:50:49 +0100
Subject: [PATCH 23/43] Add cuml solver

---
 benchmarks/ridge/solvers/cuml.py | 89 ++++++++++++++++++++++++++++++++
 1 file changed, 89 insertions(+)
 create mode 100644 benchmarks/ridge/solvers/cuml.py

diff --git a/benchmarks/ridge/solvers/cuml.py b/benchmarks/ridge/solvers/cuml.py
new file mode 100644
index 0000000..2d5efa1
--- /dev/null
+++ b/benchmarks/ridge/solvers/cuml.py
@@ -0,0 +1,89 @@
+from importlib.metadata import version
+
+from benchopt import BaseSolver, safe_import_context
+from benchopt.stopping_criterion import SingleRunCriterion
+
+with safe_import_context() as import_ctx:
+    import cuml
+    import cupy
+    import numpy as np
+
+
+class Solver(BaseSolver):
+    """https://docs.rapids.ai/api/cuml/stable/api/#ridge-regression"""
+
+    name = "cuml"
+    requirements = ["cuml"]
+
+    parameters = dict(device=["gpu"])
+
+    stopping_criterion = SingleRunCriterion(1)
+
+    def skip(self, **objective_dict):
+
+        X = objective_dict["X"]
+        if X.dtype == np.float64:
+            # We haven't came accross cuda devices that doesn't support float64 yet,
+            # can it happen ? If it happens, the following instruction will fail,
+            # please enclose it with the appropriate Try/Except to return the
+            # appropriate skip decision.
+            cupy.zeros(1, dtype=cupy.float64)
+            # return True, (
+            #     f"This {self.device} device has no support for float64 compute"
+            # )
+
+        y = objective_dict["y"]
+        if (y.ndim == 2) and (y.shape[1] > 1):
+            return True, "Multitarget is not supported."
+
+        solver = objective_dict["solver"]
+        if solver != "svd":
+            return True, "Only accepts the svd solver at the moment."
+
+        return False, None
+
+    def set_objective(
+        self,
+        X,
+        y,
+        sample_weight,
+        alpha,
+        fit_intercept,
+        solver,
+        max_iter,
+        tol,
+        random_state,
+    ):
+        self.X = cupy.asarray(X)
+        self.y = cupy.asarray(y)
+
+        self.sample_weight = sample_weight
+        if sample_weight is not None:
+            self.sample_weight = cupy.asarray(sample_weight)
+
+        self.alpha = alpha
+        self.fit_intercept = fit_intercept
+        self.solver = solver
+        self.max_iter = max_iter
+        self.tol = tol
+        self.random_state = random_state
+
+    def run(self, _):
+        estimator = cuml.Ridge(
+            alpha=self.alpha,
+            fit_intercept=self.fit_intercept,
+            solver=self.solver,
+        ).fit(self.X, self.y, sample_weight=self.sample_weight)
+
+        self.weights = estimator.coef_
+        self.intercept = estimator.intercept_
+
+    def get_result(self):
+        return dict(
+            weights=cupy.asnumpy(self.weights),
+            intercept=cupy.asnumpy(self.intercept),
+            n_iter=None,
+            version_info=f"scikit-learn {version('scikit-learn')}",
+            __name=self.name,
+            **self._parameters,
+        )

From cf662eb64ffbbbfbc263e882a20fe1c2f66551cf Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Mon, 15 Jan 2024 19:54:24 +0100
Subject: [PATCH 24/43] Add cuml results

---
 .../benchopt_run_2024-01-15_19h50m23.parquet    | Bin 0 -> 33882 bytes
 benchmarks/ridge/results.csv                    |   4 ++++
 benchmarks/ridge/solvers/cuml.py                |   7 +++++++
 3 files changed, 11 insertions(+)
 create mode 100644 benchmarks/ridge/outputs/benchopt_run_2024-01-15_19h50m23.parquet

diff --git a/benchmarks/ridge/outputs/benchopt_run_2024-01-15_19h50m23.parquet b/benchmarks/ridge/outputs/benchopt_run_2024-01-15_19h50m23.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..9ed915ec471ae2ed587f60abdf3cc130caca8f82
GIT binary patch
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diff --git a/benchmarks/ridge/results.csv b/benchmarks/ridge/results.csv
index 697cf0e..a75c863 100644
--- a/benchmarks/ridge/results.csv
+++ b/benchmarks/ridge/results.csv
@@ -1,16 +1,19 @@
 Benchmark id,Dtype,Nb data samples,Nb data features,Nb data targets,Walltime,Backend provider,Compute device,Compute runtime,Solver,Nb cpus,Cpu name,Gpu name,RAM (GB),Platform,Platform architecture,Platform release,Run date,Version info,Comment,Result nb iterations,Result objective value,Data random state,Solver random state
+3a327106,float32,5000000,100,1,1.37,cuml,gpu,,svd,32,AMD EPYC 7502 32-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,4.506280786409378,123,123
 3a327106,float32,5000000,100,1,1.73,scikit-learn,,,cholesky,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,4.506281986367035,123,123
 3a327106,float32,5000000,100,1,4.88,scikit-learn,,,lsqr,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,4.506281186382675,123,123
 3a327106,float32,5000000,100,1,16.8,scikit-learn,,,svd,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,4.506280786407089,123,123
 9bd2a256,float32,5000000,100,10,1.9,scikit-learn,,,cholesky,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.079804773100586,123,123
 9bd2a256,float32,5000000,100,10,16.9,scikit-learn,,,svd,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.079804613102112,123,123
 9bd2a256,float32,5000000,100,10,43,scikit-learn,,,lsqr,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,2.0798041330996706,123,123
+a9a05f25,float32,5000000,10,1,0.131,cuml,gpu,,svd,32,AMD EPYC 7502 32-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,0.0251269475669592,123,123
 a9a05f25,float32,5000000,10,1,0.205,scikit-learn,,,cholesky,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,0.025126947566834,123,123
 a9a05f25,float32,5000000,10,1,0.743,scikit-learn,,,svd,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,0.025126946004498,123,123
 a9a05f25,float32,5000000,10,1,0.849,scikit-learn,,,lsqr,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,0.025126947566998,123,123
 ffd32c2e,float32,5000000,10,10,0.437,scikit-learn,,,cholesky,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,0.1351635453291702,123,123
 ffd32c2e,float32,5000000,10,10,1.17,scikit-learn,,,svd,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,0.135163585330429,123,123
 ffd32c2e,float32,5000000,10,10,7.72,scikit-learn,,,lsqr,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,0.1351635853305244,123,123
+c49c3df8,float32,2000000,100,1,0.557,cuml,gpu,,svd,32,AMD EPYC 7502 32-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.283151841176033,123,123
 c49c3df8,float32,2000000,100,1,0.692,scikit-learn,,,cholesky,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.283151591079712,123,123
 c49c3df8,float32,2000000,100,1,1.92,scikit-learn,,,lsqr,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,2.2831518411445617,123,123
 c49c3df8,float32,2000000,100,1,6.06,scikit-learn,,,svd,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.283151841176987,123,123
@@ -19,6 +22,7 @@ c49c3df8,float32,2000000,100,1,6.06,scikit-learn,,,svd,32,AMD EPYC 7502 32-Core
 1a8c391a,float32,2000000,100,10,18.2,scikit-learn,,,lsqr,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,2.1605555927993776,123,123
 e5684553,float32,5000,5000,1,0.146,scikit-learn,,,lsqr,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,9.077718115234376,123,123
 e5684553,float32,5000,5000,1,0.573,scikit-learn,,,cholesky,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.20544375,123,123
+e5684553,float32,5000,5000,1,6.29,cuml,gpu,,svd,32,AMD EPYC 7502 32-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.20544453125,123,123
 e5684553,float32,5000,5000,1,10.2,scikit-learn,,,svd,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.20544296875,123,123
 7b05f2bf,float32,5000,5000,10,0.509,scikit-learn,,,cholesky,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.375214765625,123,123
 7b05f2bf,float32,5000,5000,10,1.22,scikit-learn,,,lsqr,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,9.804442734375,123,123
diff --git a/benchmarks/ridge/solvers/cuml.py b/benchmarks/ridge/solvers/cuml.py
index 2d5efa1..20482a1 100644
--- a/benchmarks/ridge/solvers/cuml.py
+++ b/benchmarks/ridge/solvers/cuml.py
@@ -68,6 +68,13 @@ def set_objective(
         self.tol = tol
         self.random_state = random_state
 
+    def warm_up(self):
+        cuml.Ridge(
+            alpha=self.alpha,
+            fit_intercept=self.fit_intercept,
+            solver=self.solver,
+        ).fit(self.X, self.y, sample_weight=self.sample_weight)
+
     def run(self, _):
         estimator = cuml.Ridge(
             alpha=self.alpha,

From f3e976551b23ea05dc59a4d4542c31179f904fd7 Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Mon, 15 Jan 2024 20:15:07 +0100
Subject: [PATCH 25/43] Also add warm-up for scikit-learn

---
 .../benchopt_run_2024-01-15_18h08m05.parquet  | Bin 33467 -> 0 bytes
 .../benchopt_run_2024-01-15_20h06m24.parquet  | Bin 0 -> 33618 bytes
 benchmarks/ridge/results.csv                  |  48 +++++++++---------
 benchmarks/ridge/solvers/scikit_learn.py      |  12 +++++
 4 files changed, 36 insertions(+), 24 deletions(-)
 delete mode 100644 benchmarks/ridge/outputs/benchopt_run_2024-01-15_18h08m05.parquet
 create mode 100644 benchmarks/ridge/outputs/benchopt_run_2024-01-15_20h06m24.parquet

diff --git a/benchmarks/ridge/outputs/benchopt_run_2024-01-15_18h08m05.parquet b/benchmarks/ridge/outputs/benchopt_run_2024-01-15_18h08m05.parquet
deleted file mode 100644
index 69c46db19a9ae775ebb580e2138822f723d2b885..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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diff --git a/benchmarks/ridge/results.csv b/benchmarks/ridge/results.csv
index a75c863..d9738d7 100644
--- a/benchmarks/ridge/results.csv
+++ b/benchmarks/ridge/results.csv
@@ -1,29 +1,29 @@
 Benchmark id,Dtype,Nb data samples,Nb data features,Nb data targets,Walltime,Backend provider,Compute device,Compute runtime,Solver,Nb cpus,Cpu name,Gpu name,RAM (GB),Platform,Platform architecture,Platform release,Run date,Version info,Comment,Result nb iterations,Result objective value,Data random state,Solver random state
 3a327106,float32,5000000,100,1,1.37,cuml,gpu,,svd,32,AMD EPYC 7502 32-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,4.506280786409378,123,123
-3a327106,float32,5000000,100,1,1.73,scikit-learn,,,cholesky,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,4.506281986367035,123,123
-3a327106,float32,5000000,100,1,4.88,scikit-learn,,,lsqr,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,4.506281186382675,123,123
-3a327106,float32,5000000,100,1,16.8,scikit-learn,,,svd,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,4.506280786407089,123,123
-9bd2a256,float32,5000000,100,10,1.9,scikit-learn,,,cholesky,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.079804773100586,123,123
-9bd2a256,float32,5000000,100,10,16.9,scikit-learn,,,svd,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.079804613102112,123,123
-9bd2a256,float32,5000000,100,10,43,scikit-learn,,,lsqr,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,2.0798041330996706,123,123
+3a327106,float32,5000000,100,1,1.73,scikit-learn,,,cholesky,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,4.506297986368179,123,123
+3a327106,float32,5000000,100,1,4.3,scikit-learn,,,lsqr,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,4.506297986381912,123,123
+3a327106,float32,5000000,100,1,13.7,scikit-learn,,,svd,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,4.506297186405945,123,123
+9bd2a256,float32,5000000,100,10,1.93,scikit-learn,,,cholesky,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.079808773100586,123,123
+9bd2a256,float32,5000000,100,10,14.9,scikit-learn,,,svd,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.079808933101807,123,123
+9bd2a256,float32,5000000,100,10,37.2,scikit-learn,,,lsqr,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,2.07980909309967,123,123
 a9a05f25,float32,5000000,10,1,0.131,cuml,gpu,,svd,32,AMD EPYC 7502 32-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,0.0251269475669592,123,123
-a9a05f25,float32,5000000,10,1,0.205,scikit-learn,,,cholesky,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,0.025126947566834,123,123
-a9a05f25,float32,5000000,10,1,0.743,scikit-learn,,,svd,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,0.025126946004498,123,123
-a9a05f25,float32,5000000,10,1,0.849,scikit-learn,,,lsqr,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,0.025126947566998,123,123
-ffd32c2e,float32,5000000,10,10,0.437,scikit-learn,,,cholesky,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,0.1351635453291702,123,123
-ffd32c2e,float32,5000000,10,10,1.17,scikit-learn,,,svd,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,0.135163585330429,123,123
-ffd32c2e,float32,5000000,10,10,7.72,scikit-learn,,,lsqr,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,0.1351635853305244,123,123
+a9a05f25,float32,5000000,10,1,0.245,scikit-learn,,,cholesky,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,0.02512695694184,123,123
+a9a05f25,float32,5000000,10,1,0.796,scikit-learn,,,svd,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,0.025126958504486,123,123
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+ffd32c2e,float32,5000000,10,10,1.01,scikit-learn,,,svd,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,0.1351636653303528,123,123
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-c49c3df8,float32,2000000,100,1,0.692,scikit-learn,,,cholesky,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.283151591079712,123,123
-c49c3df8,float32,2000000,100,1,1.92,scikit-learn,,,lsqr,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,2.2831518411445617,123,123
-c49c3df8,float32,2000000,100,1,6.06,scikit-learn,,,svd,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.283151841176987,123,123
-1a8c391a,float32,2000000,100,10,0.761,scikit-learn,,,cholesky,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.160555392800904,123,123
-1a8c391a,float32,2000000,100,10,6.41,scikit-learn,,,svd,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.160555392799377,123,123
-1a8c391a,float32,2000000,100,10,18.2,scikit-learn,,,lsqr,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,2.1605555927993776,123,123
-e5684553,float32,5000,5000,1,0.146,scikit-learn,,,lsqr,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,9.077718115234376,123,123
-e5684553,float32,5000,5000,1,0.573,scikit-learn,,,cholesky,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.20544375,123,123
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+c49c3df8,float32,2000000,100,1,1.84,scikit-learn,,,lsqr,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,2.283155091144562,123,123
+c49c3df8,float32,2000000,100,1,5.62,scikit-learn,,,svd,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.2831550911808014,123,123
+1a8c391a,float32,2000000,100,10,0.779,scikit-learn,,,cholesky,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.160573592800904,123,123
+1a8c391a,float32,2000000,100,10,5.7,scikit-learn,,,svd,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.1605733927993773,123,123
+1a8c391a,float32,2000000,100,10,16.4,scikit-learn,,,lsqr,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,2.1605733928001403,123,123
+e5684553,float32,5000,5000,1,0.166,scikit-learn,,,lsqr,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,9.347395458984376,123,123
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 e5684553,float32,5000,5000,1,6.29,cuml,gpu,,svd,32,AMD EPYC 7502 32-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.20544453125,123,123
-e5684553,float32,5000,5000,1,10.2,scikit-learn,,,svd,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.20544296875,123,123
-7b05f2bf,float32,5000,5000,10,0.509,scikit-learn,,,cholesky,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.375214765625,123,123
-7b05f2bf,float32,5000,5000,10,1.22,scikit-learn,,,lsqr,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,9.804442734375,123,123
-7b05f2bf,float32,5000,5000,10,10.2,scikit-learn,,,svd,32,AMD EPYC 7502 32-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.37521484375,123,123
+e5684553,float32,5000,5000,1,9.08,scikit-learn,,,svd,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.459914453125,123,123
+7b05f2bf,float32,5000,5000,10,0.478,scikit-learn,,,cholesky,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.781028203125,123,123
+7b05f2bf,float32,5000,5000,10,1.22,scikit-learn,,,lsqr,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,9.960193984375,123,123
+7b05f2bf,float32,5000,5000,10,9.23,scikit-learn,,,svd,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.781131640625,123,123
diff --git a/benchmarks/ridge/solvers/scikit_learn.py b/benchmarks/ridge/solvers/scikit_learn.py
index 0d49ac0..0fbbe8b 100644
--- a/benchmarks/ridge/solvers/scikit_learn.py
+++ b/benchmarks/ridge/solvers/scikit_learn.py
@@ -47,6 +47,18 @@ def skip(self, **objective_dict):
 
         return False, None
 
+    def warm_up(self):
+        Ridge(
+            alpha=self.alpha,
+            fit_intercept=self.fit_intercept,
+            copy_X=False,
+            max_iter=self.max_iter,
+            tol=self.tol,
+            solver=self.solver,
+            positive=True if (self.solver == "lbfgs") else False,
+            random_state=self.random_state,
+        ).fit(self.X, self.y, self.sample_weight)
+
     def run(self, _):
         estimator = Ridge(
             alpha=self.alpha,

From c2f2479527f5b3010092a4eb5be2bc3664a1eca1 Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Mon, 15 Jan 2024 20:20:27 +0100
Subject: [PATCH 26/43] Faster warmup

---
 benchmarks/ridge/solvers/cuml.py         | 5 ++++-
 benchmarks/ridge/solvers/scikit_learn.py | 5 ++++-
 2 files changed, 8 insertions(+), 2 deletions(-)

diff --git a/benchmarks/ridge/solvers/cuml.py b/benchmarks/ridge/solvers/cuml.py
index 20482a1..c64cd41 100644
--- a/benchmarks/ridge/solvers/cuml.py
+++ b/benchmarks/ridge/solvers/cuml.py
@@ -69,11 +69,14 @@ def set_objective(
         self.random_state = random_state
 
     def warm_up(self):
+        sample_weight = self.sample_weight
+        if sample_weight is not None:
+            sample_weight = sample_weight[:2]
         cuml.Ridge(
             alpha=self.alpha,
             fit_intercept=self.fit_intercept,
             solver=self.solver,
-        ).fit(self.X, self.y, sample_weight=self.sample_weight)
+        ).fit(self.X[:2], self.y[:2], sample_weight=sample_weight)
 
     def run(self, _):
         estimator = cuml.Ridge(
diff --git a/benchmarks/ridge/solvers/scikit_learn.py b/benchmarks/ridge/solvers/scikit_learn.py
index 0fbbe8b..97db1c6 100644
--- a/benchmarks/ridge/solvers/scikit_learn.py
+++ b/benchmarks/ridge/solvers/scikit_learn.py
@@ -48,6 +48,9 @@ def skip(self, **objective_dict):
         return False, None
 
     def warm_up(self):
+        sample_weight = self.sample_weight
+        if sample_weight is not None:
+            sample_weight = sample_weight[:2]
         Ridge(
             alpha=self.alpha,
             fit_intercept=self.fit_intercept,
@@ -57,7 +60,7 @@ def warm_up(self):
             solver=self.solver,
             positive=True if (self.solver == "lbfgs") else False,
             random_state=self.random_state,
-        ).fit(self.X, self.y, self.sample_weight)
+        ).fit(self.X[:2], self.y[:2], sample_weight)
 
     def run(self, _):
         estimator = Ridge(

From edd2e90be7fadae5196abe9017d6bdc82a184ad4 Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Tue, 16 Jan 2024 14:27:10 +0100
Subject: [PATCH 27/43] Add sklearn+array_api+torch solver

---
 .../ridge/solvers/sklearn_torch_dispatch.py   | 129 ++++++++++++++++++
 1 file changed, 129 insertions(+)
 create mode 100644 benchmarks/ridge/solvers/sklearn_torch_dispatch.py

diff --git a/benchmarks/ridge/solvers/sklearn_torch_dispatch.py b/benchmarks/ridge/solvers/sklearn_torch_dispatch.py
new file mode 100644
index 0000000..f02eb2b
--- /dev/null
+++ b/benchmarks/ridge/solvers/sklearn_torch_dispatch.py
@@ -0,0 +1,129 @@
+from importlib.metadata import PackageNotFoundError, version
+
+from benchopt import BaseSolver, safe_import_context
+from benchopt.stopping_criterion import SingleRunCriterion
+
+with safe_import_context() as import_ctx:
+    # isort: off
+    import numpy as np
+
+    # NB: even if it's not use for the compute we rely on the sklearn_pytorch_engine
+    # for the few utilities it contains e.g for loading torch with xpu support and
+    # checking for float64 compat.
+    # This import is necessary to pre-load torch extensions
+    import sklearn_pytorch_engine  # noqa
+    import torch
+    from sklearn import config_context
+    from sklearn.linear_models import Ridge
+    from sklearn_pytorch_engine._utils import has_fp64_support
+
+    # isort: on
+
+
+class Solver(BaseSolver):
+    name = "sklearn-torch-dispatch"
+    requirements = ["scikit-learn", "sklearn-pytorch-engine"]
+
+    parameters = {
+        "device": ["cpu", "xpu", "cuda", "mps"],
+    }
+
+    stopping_criterion = SingleRunCriterion(1)
+
+    def skip(self, **objective_dict):
+        try:
+            torch.zeros(1, dtype=torch.float32, device=self.device)
+        except Exception:
+            return True, f"{self.device} compute backend for pytorch not found"
+
+        X = objective_dict["X"]
+        if (X.dtype == np.float64) and not has_fp64_support(self.device):
+            return True, (
+                f"This {self.device} device has no support for float64 compute"
+            )
+
+        solver = objective_dict["solver"]
+        if solver != "svd":
+            return True, "Only accepts the svd solver at the moment."
+
+        return False, None
+
+    def set_objective(
+        self,
+        X,
+        y,
+        sample_weight,
+        alpha,
+        fit_intercept,
+        solver,
+        max_iter,
+        tol,
+        random_state,
+    ):
+        # Copy the data before running the benchmark to ensure that no unfortunate side
+        # effects can happen
+        self.X = torch.asarray(X, copy=True, device=self.device)
+        self.y = torch.asarray(y, copy=True, device=self.device)
+        self.sample_weight = sample_weight
+        if sample_weight is not None:
+            self.sample_weight = torch.asarray(
+                sample_weight, copy=True, device=self.device
+            )
+
+        self.alpha = alpha
+        self.fit_intercept = fit_intercept
+        self.solver = solver
+        self.max_iter = max_iter
+        self.tol = tol
+        self.random_state = random_state
+
+    def warm_up(self):
+        sample_weight = self.sample_weight
+        if sample_weight is not None:
+            sample_weight = sample_weight[:2]
+        with config_context(array_api_dispatch=True):
+            Ridge(
+                alpha=self.alpha,
+                fit_intercept=self.fit_intercept,
+                copy_X=False,
+                max_iter=self.max_iter,
+                tol=self.tol,
+                solver=self.solver,
+                positive=True if (self.solver == "lbfgs") else False,
+                random_state=self.random_state,
+            ).fit(self.X[:2], self.y[:2], sample_weight)
+
+    def run(self, _):
+        with config_context(array_api_dispatch=True):
+            estimator = Ridge(
+                alpha=self.alpha,
+                fit_intercept=self.fit_intercept,
+                copy_X=False,
+                max_iter=self.max_iter,
+                tol=self.tol,
+                solver=self.solver,
+                positive=True if (self.solver == "lbfgs") else False,
+                random_state=self.random_state,
+            ).fit(self.X, self.y, self.sample_weight)
+
+        self.weights = estimator.coef_
+        self.intercept = estimator.intercept_
+        self.n_iter_ = estimator.n_iter_
+
+    def get_result(self):
+        version_info = (
+            f"scikit-learn {version('scikit-learn')}; torch {version('torch')}"
+        )
+        try:
+            version_info += f"; ipex {version('intel-extension-for-pytorch')}"
+        except PackageNotFoundError:
+            pass
+
+        return dict(
+            weights=self.weights.cpu().asnumpy(),
+            intercept=self.intercept.cpu().asnumpy(),
+            n_iter=self.n_iter_.cpu(),
+            version_info=version_info,
+            __name=self.name,
+            **self._parameters,
+        )

From 4e1a2c0dee79df7a3d8b4772cac51c64a69cca55 Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Tue, 16 Jan 2024 15:04:11 +0100
Subject: [PATCH 28/43] Fix sklearn+array api+torch solver, and add results

---
 .../benchopt_run_2024-01-16_14h54m53.parquet    | Bin 0 -> 34246 bytes
 benchmarks/ridge/results.csv                    |  16 ++++++++++++++++
 .../ridge/solvers/sklearn_torch_dispatch.py     |   8 ++++----
 3 files changed, 20 insertions(+), 4 deletions(-)
 create mode 100644 benchmarks/ridge/outputs/benchopt_run_2024-01-16_14h54m53.parquet

diff --git a/benchmarks/ridge/outputs/benchopt_run_2024-01-16_14h54m53.parquet b/benchmarks/ridge/outputs/benchopt_run_2024-01-16_14h54m53.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..84c8b668348291f551680ab2c1cad1d73e0fe9c1
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diff --git a/benchmarks/ridge/results.csv b/benchmarks/ridge/results.csv
index d9738d7..f334b2f 100644
--- a/benchmarks/ridge/results.csv
+++ b/benchmarks/ridge/results.csv
@@ -1,29 +1,45 @@
 Benchmark id,Dtype,Nb data samples,Nb data features,Nb data targets,Walltime,Backend provider,Compute device,Compute runtime,Solver,Nb cpus,Cpu name,Gpu name,RAM (GB),Platform,Platform architecture,Platform release,Run date,Version info,Comment,Result nb iterations,Result objective value,Data random state,Solver random state
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 3a327106,float32,5000000,100,1,1.37,cuml,gpu,,svd,32,AMD EPYC 7502 32-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,4.506280786409378,123,123
 3a327106,float32,5000000,100,1,1.73,scikit-learn,,,cholesky,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,4.506297986368179,123,123
 3a327106,float32,5000000,100,1,4.3,scikit-learn,,,lsqr,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,4.506297986381912,123,123
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 3a327106,float32,5000000,100,1,13.7,scikit-learn,,,svd,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,4.506297186405945,123,123
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 9bd2a256,float32,5000000,100,10,1.93,scikit-learn,,,cholesky,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.079808773100586,123,123
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 9bd2a256,float32,5000000,100,10,37.2,scikit-learn,,,lsqr,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,2.07980909309967,123,123
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 a9a05f25,float32,5000000,10,1,0.131,cuml,gpu,,svd,32,AMD EPYC 7502 32-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,0.0251269475669592,123,123
 a9a05f25,float32,5000000,10,1,0.245,scikit-learn,,,cholesky,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,0.02512695694184,123,123
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 a9a05f25,float32,5000000,10,1,0.796,scikit-learn,,,svd,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,0.025126958504486,123,123
 a9a05f25,float32,5000000,10,1,0.842,scikit-learn,,,lsqr,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,0.025126955379498,123,123
+ffd32c2e,float32,5000000,10,10,0.0375,sklearn-torch-dispatch,cuda,,svd,32,AMD EPYC 7302 16-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,0.1351635653306007,123,123
 ffd32c2e,float32,5000000,10,10,0.441,scikit-learn,,,cholesky,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,0.1351636653292465,123,123
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 ffd32c2e,float32,5000000,10,10,7.28,scikit-learn,,,lsqr,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,0.1351636553305053,123,123
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 c49c3df8,float32,2000000,100,1,0.557,cuml,gpu,,svd,32,AMD EPYC 7502 32-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.283151841176033,123,123
 c49c3df8,float32,2000000,100,1,0.727,scikit-learn,,,cholesky,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.283155591078758,123,123
 c49c3df8,float32,2000000,100,1,1.84,scikit-learn,,,lsqr,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,2.283155091144562,123,123
+c49c3df8,float32,2000000,100,1,2.35,sklearn-torch-dispatch,cpu,,svd,32,AMD EPYC 7302 16-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,2.2831518412952425,123,123
 c49c3df8,float32,2000000,100,1,5.62,scikit-learn,,,svd,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.2831550911808014,123,123
+1a8c391a,float32,2000000,100,10,0.282,sklearn-torch-dispatch,cuda,,svd,32,AMD EPYC 7302 16-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,2.160555392803192,123,123
 1a8c391a,float32,2000000,100,10,0.779,scikit-learn,,,cholesky,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.160573592800904,123,123
+1a8c391a,float32,2000000,100,10,2.61,sklearn-torch-dispatch,cpu,,svd,32,AMD EPYC 7302 16-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,2.16055519289856,123,123
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 1a8c391a,float32,2000000,100,10,16.4,scikit-learn,,,lsqr,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,2.1605733928001403,123,123
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 e5684553,float32,5000,5000,1,6.29,cuml,gpu,,svd,32,AMD EPYC 7502 32-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.20544453125,123,123
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 7b05f2bf,float32,5000,5000,10,0.478,scikit-learn,,,cholesky,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.781028203125,123,123
 7b05f2bf,float32,5000,5000,10,1.22,scikit-learn,,,lsqr,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,9.960193984375,123,123
+7b05f2bf,float32,5000,5000,10,6.82,sklearn-torch-dispatch,cpu,,svd,32,AMD EPYC 7302 16-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,2.747505859375,123,123
 7b05f2bf,float32,5000,5000,10,9.23,scikit-learn,,,svd,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.781131640625,123,123
+7b05f2bf,float32,5000,5000,10,10.7,sklearn-torch-dispatch,cuda,,svd,32,AMD EPYC 7302 16-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,2.747708828125,123,123
diff --git a/benchmarks/ridge/solvers/sklearn_torch_dispatch.py b/benchmarks/ridge/solvers/sklearn_torch_dispatch.py
index f02eb2b..ceef958 100644
--- a/benchmarks/ridge/solvers/sklearn_torch_dispatch.py
+++ b/benchmarks/ridge/solvers/sklearn_torch_dispatch.py
@@ -14,7 +14,7 @@
     import sklearn_pytorch_engine  # noqa
     import torch
     from sklearn import config_context
-    from sklearn.linear_models import Ridge
+    from sklearn.linear_model import Ridge
     from sklearn_pytorch_engine._utils import has_fp64_support
 
     # isort: on
@@ -120,9 +120,9 @@ def get_result(self):
             pass
 
         return dict(
-            weights=self.weights.cpu().asnumpy(),
-            intercept=self.intercept.cpu().asnumpy(),
-            n_iter=self.n_iter_.cpu(),
+            weights=self.weights.cpu().numpy(),
+            intercept=self.intercept.cpu().numpy(),
+            n_iter=self.n_iter_,
             version_info=version_info,
             __name=self.name,
             **self._parameters,

From 70ac835d4390f7a4a952e15a709fae28a5c75994 Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Tue, 16 Jan 2024 16:03:10 +0100
Subject: [PATCH 29/43] Add scikit-learn-intelex solver

---
 benchmarks/ridge/objective.py                 |   3 +
 benchmarks/ridge/solvers/scikit_learn.py      |   3 +
 .../ridge/solvers/scikit_learn_intelex.py     | 142 ++++++++++++++++++
 3 files changed, 148 insertions(+)
 create mode 100644 benchmarks/ridge/solvers/scikit_learn_intelex.py

diff --git a/benchmarks/ridge/objective.py b/benchmarks/ridge/objective.py
index 970fb21..45f5e9c 100644
--- a/benchmarks/ridge/objective.py
+++ b/benchmarks/ridge/objective.py
@@ -29,6 +29,9 @@ class Objective(BaseObjective):
             ("sag", 50, 0),
             ("saga", 25, 0),
             ("lbfgs", 100, 0),
+            # Used for solvers like scikit-learn-intelex that doesn't
+            # document the underlying solver nor expose the n_iter_ attribute
+            ("???", None, 0),
         ],
         "sample_weight": ["None"],  # NB: add "random" to test non None weights
         "random_state": [123],
diff --git a/benchmarks/ridge/solvers/scikit_learn.py b/benchmarks/ridge/solvers/scikit_learn.py
index 97db1c6..4a2d278 100644
--- a/benchmarks/ridge/solvers/scikit_learn.py
+++ b/benchmarks/ridge/solvers/scikit_learn.py
@@ -45,6 +45,9 @@ def skip(self, **objective_dict):
                 "in the benchmark."
             )
 
+        if solver == "???":
+            return True, ""
+
         return False, None
 
     def warm_up(self):
diff --git a/benchmarks/ridge/solvers/scikit_learn_intelex.py b/benchmarks/ridge/solvers/scikit_learn_intelex.py
new file mode 100644
index 0000000..88cb36f
--- /dev/null
+++ b/benchmarks/ridge/solvers/scikit_learn_intelex.py
@@ -0,0 +1,142 @@
+from contextlib import nullcontext
+from importlib.metadata import version
+
+from benchopt import BaseSolver, safe_import_context
+from benchopt.stopping_criterion import SingleRunCriterion
+
+with safe_import_context() as import_ctx:
+    # isort: off
+    import dpctl
+    import numpy as np
+    from sklearnex.linear_model import Ridge
+    from sklearnex import config_context
+
+    # isort: on
+
+
+class Solver(BaseSolver):
+    name = "scikit-learn-intelex"
+
+    requirements = [
+        "scikit-learn-intelex",
+        "dpcpp-cpp-rt",
+    ]
+
+    parameters = {
+        "device, runtime": [
+            ("cpu", None),  # TODO: replace "None" with "opencl" if relevant
+            ("gpu", "level_zero"),
+        ],
+    }
+
+    stopping_criterion = SingleRunCriterion(1)
+
+    def skip(self, **objective_dict):
+        if self.runtime is not None:
+            try:
+                device = dpctl.SyclDevice(f"{self.runtime}:{self.device}")
+            except Exception:
+                return (
+                    True,
+                    f"{self.runtime} runtime not found for device {self.device}",
+                )
+
+            X = objective_dict["X"]
+            if (X.dtype == np.float64) and not device.has_aspect_fp64:
+                return True, (
+                    f"This {self.device} device has no support for float64 compute"
+                )
+
+        solver = objective_dict["solver"]
+
+        if solver != "???":
+            # TODO: investigate ?
+            return True, "The underlying solver is unknown."
+
+        return False, None
+
+    def set_objective(
+        self,
+        X,
+        y,
+        sample_weight,
+        alpha,
+        fit_intercept,
+        solver,
+        max_iter,
+        tol,
+        random_state,
+    ):
+        # TODO: the overhead of the copy of the data from host to device could be
+        # eliminated if scikit-learn-intelex could just take usm_ndarray objects as
+        # input and directly run compute with the underlying memory buffer. The
+        # documentation at
+        # https://intel.github.io/scikit-learn-intelex/latest/oneapi-gpu.html#device-offloading  # noqa
+        # suggests that it is the intended behavior, however in practice
+        # scikit-learn-intelex currently always perform underlying copies
+        # under the hood no matter what, and sometimes fails at doing so. See e.g.
+        # issue at
+        # https://github.com/intel/scikit-learn-intelex/issues/1534#issuecomment-1766266299  # noqa
+
+        # if self.runtime != "numpy":
+        #     device = device = dpctl.SyclDevice(f"{self.runtime}:{self.device}")
+        #     self.X = dpt.asarray(X, copy=True, device=device)
+        # else:
+        #     self.X = X
+
+        self.X = X
+        self.y = y
+        self.sample_weight = sample_weight
+        self.alpha = alpha
+        self.fit_intercept = fit_intercept
+        self.solver = solver
+        self.max_iter = max_iter
+        self.tol = tol
+        self.random_state = random_state
+
+    def warm_up(self):
+        sample_weight = self.sample_weight
+        if sample_weight is not None:
+            sample_weight = sample_weight[:2]
+        with nullcontext() if (self.runtime is None) else config_context(
+            target_offload=f"{self.runtime}:{self.device}"
+        ):
+            Ridge(
+                alpha=self.alpha,
+                fit_intercept=self.fit_intercept,
+                copy_X=False,
+                max_iter=self.max_iter,
+                tol=self.tol,
+                solver="auto",
+                positive=True if (self.solver == "lbfgs") else False,
+                random_state=self.random_state,
+            ).fit(self.X[:2], self.y[:2], sample_weight)
+
+    def run(self, _):
+        with nullcontext() if (self.runtime is None) else config_context(
+            target_offload=f"{self.runtime}:{self.device}"
+        ):
+            estimator = Ridge(
+                alpha=self.alpha,
+                fit_intercept=self.fit_intercept,
+                copy_X=False,
+                max_iter=self.max_iter,
+                tol=self.tol,
+                solver="auto",
+                positive=True if (self.solver == "lbfgs") else False,
+                random_state=self.random_state,
+            ).fit(self.X, self.y, self.sample_weight)
+
+        self.weights = estimator.coef_
+        self.intercept = estimator.intercept_
+        self.n_iter_ = estimator.n_iter_
+
+    def get_result(self):
+        return dict(
+            weights=self.weights,
+            intercept=self.intercept,
+            n_iter=self.n_iter_,
+            version_info=f"scikit-learn-intelex {version('scikit-learn-intelex')}",
+            __name=self.name,
+            **self._parameters,
+        )

From 9e808cd8bf7373ec8c877f80634abbe150cb236a Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Tue, 16 Jan 2024 16:11:36 +0100
Subject: [PATCH 30/43] Add scikit-learn-intelex result even if actual solver
 is unkown

---
 .../benchopt_run_2024-01-16_16h08m16.parquet    | Bin 0 -> 34664 bytes
 benchmarks/ridge/results.csv                    |   8 ++++++++
 2 files changed, 8 insertions(+)
 create mode 100644 benchmarks/ridge/outputs/benchopt_run_2024-01-16_16h08m16.parquet

diff --git a/benchmarks/ridge/outputs/benchopt_run_2024-01-16_16h08m16.parquet b/benchmarks/ridge/outputs/benchopt_run_2024-01-16_16h08m16.parquet
new file mode 100644
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diff --git a/benchmarks/ridge/results.csv b/benchmarks/ridge/results.csv
index f334b2f..9df6e45 100644
--- a/benchmarks/ridge/results.csv
+++ b/benchmarks/ridge/results.csv
@@ -1,15 +1,18 @@
 Benchmark id,Dtype,Nb data samples,Nb data features,Nb data targets,Walltime,Backend provider,Compute device,Compute runtime,Solver,Nb cpus,Cpu name,Gpu name,RAM (GB),Platform,Platform architecture,Platform release,Run date,Version info,Comment,Result nb iterations,Result objective value,Data random state,Solver random state
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 3a327106,float32,5000000,100,1,0.735,sklearn-torch-dispatch,cuda,,svd,32,AMD EPYC 7302 16-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,4.506280786413193,123,123
 3a327106,float32,5000000,100,1,1.37,cuml,gpu,,svd,32,AMD EPYC 7502 32-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,4.506280786409378,123,123
 3a327106,float32,5000000,100,1,1.73,scikit-learn,,,cholesky,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,4.506297986368179,123,123
 3a327106,float32,5000000,100,1,4.3,scikit-learn,,,lsqr,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,4.506297986381912,123,123
 3a327106,float32,5000000,100,1,5.56,sklearn-torch-dispatch,cpu,,svd,32,AMD EPYC 7302 16-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,4.506281586742783,123,123
 3a327106,float32,5000000,100,1,13.7,scikit-learn,,,svd,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,4.506297186405945,123,123
+9bd2a256,float32,5000000,100,10,0.26,scikit-learn-intelex,cpu,,???,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn-intelex 2024.0.1,,,2.079804933099976,123,123
 9bd2a256,float32,5000000,100,10,0.74,sklearn-torch-dispatch,cuda,,svd,32,AMD EPYC 7302 16-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,2.0798047731054687,123,123
 9bd2a256,float32,5000000,100,10,1.93,scikit-learn,,,cholesky,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.079808773100586,123,123
 9bd2a256,float32,5000000,100,10,5.78,sklearn-torch-dispatch,cpu,,svd,32,AMD EPYC 7302 16-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,2.0798046132958983,123,123
 9bd2a256,float32,5000000,100,10,14.9,scikit-learn,,,svd,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.079808933101807,123,123
 9bd2a256,float32,5000000,100,10,37.2,scikit-learn,,,lsqr,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,2.07980909309967,123,123
+a9a05f25,float32,5000000,10,1,0.0273,scikit-learn-intelex,cpu,,???,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn-intelex 2024.0.1,,,0.0251269475670009,123,123
 a9a05f25,float32,5000000,10,1,0.0324,sklearn-torch-dispatch,cuda,,svd,32,AMD EPYC 7302 16-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,0.0251269460045129,123,123
 a9a05f25,float32,5000000,10,1,0.131,cuml,gpu,,svd,32,AMD EPYC 7502 32-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,0.0251269475669592,123,123
 a9a05f25,float32,5000000,10,1,0.245,scikit-learn,,,cholesky,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,0.02512695694184,123,123
@@ -17,16 +20,19 @@ a9a05f25,float32,5000000,10,1,0.527,sklearn-torch-dispatch,cpu,,svd,32,AMD EPYC
 a9a05f25,float32,5000000,10,1,0.796,scikit-learn,,,svd,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,0.025126958504486,123,123
 a9a05f25,float32,5000000,10,1,0.842,scikit-learn,,,lsqr,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,0.025126955379498,123,123
 ffd32c2e,float32,5000000,10,10,0.0375,sklearn-torch-dispatch,cuda,,svd,32,AMD EPYC 7302 16-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,0.1351635653306007,123,123
+ffd32c2e,float32,5000000,10,10,0.0458,scikit-learn-intelex,cpu,,???,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn-intelex 2024.0.1,,,0.1351635853306198,123,123
 ffd32c2e,float32,5000000,10,10,0.441,scikit-learn,,,cholesky,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,0.1351636653292465,123,123
 ffd32c2e,float32,5000000,10,10,0.688,sklearn-torch-dispatch,cpu,,svd,32,AMD EPYC 7302 16-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,0.1351635653207397,123,123
 ffd32c2e,float32,5000000,10,10,1.01,scikit-learn,,,svd,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,0.1351636653303528,123,123
 ffd32c2e,float32,5000000,10,10,7.28,scikit-learn,,,lsqr,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,0.1351636553305053,123,123
+c49c3df8,float32,2000000,100,1,0.0924,scikit-learn-intelex,cpu,,???,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn-intelex 2024.0.1,,,2.283151841181755,123,123
 c49c3df8,float32,2000000,100,1,0.283,sklearn-torch-dispatch,cuda,,svd,32,AMD EPYC 7302 16-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,2.28315184118557,123,123
 c49c3df8,float32,2000000,100,1,0.557,cuml,gpu,,svd,32,AMD EPYC 7502 32-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.283151841176033,123,123
 c49c3df8,float32,2000000,100,1,0.727,scikit-learn,,,cholesky,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.283155591078758,123,123
 c49c3df8,float32,2000000,100,1,1.84,scikit-learn,,,lsqr,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,2.283155091144562,123,123
 c49c3df8,float32,2000000,100,1,2.35,sklearn-torch-dispatch,cpu,,svd,32,AMD EPYC 7302 16-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,2.2831518412952425,123,123
 c49c3df8,float32,2000000,100,1,5.62,scikit-learn,,,svd,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.2831550911808014,123,123
+1a8c391a,float32,2000000,100,10,0.0961,scikit-learn-intelex,cpu,,???,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn-intelex 2024.0.1,,,2.160555392794037,123,123
 1a8c391a,float32,2000000,100,10,0.282,sklearn-torch-dispatch,cuda,,svd,32,AMD EPYC 7302 16-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,2.160555392803192,123,123
 1a8c391a,float32,2000000,100,10,0.779,scikit-learn,,,cholesky,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.160573592800904,123,123
 1a8c391a,float32,2000000,100,10,2.61,sklearn-torch-dispatch,cpu,,svd,32,AMD EPYC 7302 16-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,2.16055519289856,123,123
@@ -34,11 +40,13 @@ c49c3df8,float32,2000000,100,1,5.62,scikit-learn,,,svd,32,AMD EPYC 7402 24-Core
 1a8c391a,float32,2000000,100,10,16.4,scikit-learn,,,lsqr,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,2.1605733928001403,123,123
 e5684553,float32,5000,5000,1,0.166,scikit-learn,,,lsqr,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,9.347395458984376,123,123
 e5684553,float32,5000,5000,1,0.541,scikit-learn,,,cholesky,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.45976669921875,123,123
+e5684553,float32,5000,5000,1,0.557,scikit-learn-intelex,cpu,,???,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn-intelex 2024.0.1,,,3.76711484375,123,123
 e5684553,float32,5000,5000,1,6.29,cuml,gpu,,svd,32,AMD EPYC 7502 32-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.20544453125,123,123
 e5684553,float32,5000,5000,1,7.03,sklearn-torch-dispatch,cpu,,svd,32,AMD EPYC 7302 16-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,3.76734375,123,123
 e5684553,float32,5000,5000,1,9.08,scikit-learn,,,svd,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.459914453125,123,123
 e5684553,float32,5000,5000,1,10.7,sklearn-torch-dispatch,cuda,,svd,32,AMD EPYC 7302 16-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,3.76877265625,123,123
 7b05f2bf,float32,5000,5000,10,0.478,scikit-learn,,,cholesky,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.781028203125,123,123
+7b05f2bf,float32,5000,5000,10,0.532,scikit-learn-intelex,cpu,,???,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn-intelex 2024.0.1,,,2.74752203125,123,123
 7b05f2bf,float32,5000,5000,10,1.22,scikit-learn,,,lsqr,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,9.960193984375,123,123
 7b05f2bf,float32,5000,5000,10,6.82,sklearn-torch-dispatch,cpu,,svd,32,AMD EPYC 7302 16-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,2.747505859375,123,123
 7b05f2bf,float32,5000,5000,10,9.23,scikit-learn,,,svd,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.781131640625,123,123

From d6cc6927d43e3deeb690b90b6f63fe447b92d5e5 Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Tue, 16 Jan 2024 16:24:24 +0100
Subject: [PATCH 31/43] Skip if array_api_support for Ridge not available

---
 benchmarks/ridge/solvers/sklearn_torch_dispatch.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/benchmarks/ridge/solvers/sklearn_torch_dispatch.py b/benchmarks/ridge/solvers/sklearn_torch_dispatch.py
index ceef958..c1e4553 100644
--- a/benchmarks/ridge/solvers/sklearn_torch_dispatch.py
+++ b/benchmarks/ridge/solvers/sklearn_torch_dispatch.py
@@ -31,6 +31,11 @@ class Solver(BaseSolver):
     stopping_criterion = SingleRunCriterion(1)
 
     def skip(self, **objective_dict):
+        if not Ridge()._get_tags()["array_api_support"]:
+            return True, (
+                "Requires the development branch for Ridge support for Array API."
+            )
+
         try:
             torch.zeros(1, dtype=torch.float32, device=self.device)
         except Exception:

From 6bcf4d0644b2932fcab80958c327fe554f7917f0 Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Wed, 17 Jan 2024 10:50:08 +0100
Subject: [PATCH 32/43] wip: iterate on benchmark parameters, following review
 suggestions

---
 benchmarks/ridge/consolidate_result_csv.py    |  5 +++++
 benchmarks/ridge/datasets/simulated_blobs.py  |  7 +++----
 benchmarks/ridge/objective.py                 | 20 +++++++++----------
 benchmarks/ridge/solvers/scikit_learn.py      |  4 ++--
 .../ridge/solvers/scikit_learn_intelex.py     |  4 ++--
 5 files changed, 22 insertions(+), 18 deletions(-)

diff --git a/benchmarks/ridge/consolidate_result_csv.py b/benchmarks/ridge/consolidate_result_csv.py
index b2cb42f..76cf2de 100644
--- a/benchmarks/ridge/consolidate_result_csv.py
+++ b/benchmarks/ridge/consolidate_result_csv.py
@@ -33,6 +33,7 @@
 COMPUTE_DEVICE = "Compute device"
 COMPUTE_RUNTIME = "Compute runtime"
 DATA_RANDOM_STATE = "Data random state"
+REGULARIZATION_STRENGTH = "Regularization strength"
 DTYPE = "Dtype"
 NB_DATA_FEATURES = "Nb data features"
 NB_DATA_SAMPLES = "Nb data samples"
@@ -60,6 +61,7 @@
     NB_DATA_SAMPLES,
     NB_DATA_FEATURES,
     NB_DATA_TARGETS,
+    REGULARIZATION_STRENGTH,
     WALLTIME,
     BACKEND_PROVIDER,
     COMPUTE_DEVICE,
@@ -87,6 +89,7 @@
     NB_DATA_SAMPLES: np.int64,
     NB_DATA_FEATURES: np.int64,
     NB_DATA_TARGETS: np.int64,
+    REGULARIZATION_STRENGTH: np.float64,
     WALLTIME: np.float64,
     BACKEND_PROVIDER: str,
     COMPUTE_DEVICE: str,
@@ -120,6 +123,7 @@
     NB_DATA_SAMPLES,
     NB_DATA_FEATURES,
     NB_DATA_TARGETS,
+    REGULARIZATION_STRENGTH,
     BACKEND_PROVIDER,
     SOLVER,
     COMPUTE_DEVICE,
@@ -139,6 +143,7 @@
     (NB_DATA_SAMPLES, False),
     (NB_DATA_FEATURES, False),
     (NB_DATA_TARGETS, True),
+    (REGULARIZATION_STRENGTH, False),
     (WALLTIME, True),
     (BACKEND_PROVIDER, True),
     (COMPUTE_DEVICE, True),
diff --git a/benchmarks/ridge/datasets/simulated_blobs.py b/benchmarks/ridge/datasets/simulated_blobs.py
index 68e40f4..5d3276a 100644
--- a/benchmarks/ridge/datasets/simulated_blobs.py
+++ b/benchmarks/ridge/datasets/simulated_blobs.py
@@ -10,10 +10,9 @@ class Dataset(BaseDataset):
 
     parameters = {
         "n_samples, n_features": [
-            (5_000_000, 100),
-            (5000, 5000),
-            (5_000_000, 10),
-            (2_000_000, 100),
+            (10_000_000, 100),
+            (10_000_000, 10),
+            (5000, 10000),
         ],
         "n_targets": [1, 10],
         "dtype": ["float32"],
diff --git a/benchmarks/ridge/objective.py b/benchmarks/ridge/objective.py
index 45f5e9c..4681241 100644
--- a/benchmarks/ridge/objective.py
+++ b/benchmarks/ridge/objective.py
@@ -19,19 +19,19 @@ class Objective(BaseObjective):
     # for all solvers, rather than being an independent benchmark space for each
     # solver.
     parameters = {
-        "alpha": [1.0],
+        "alpha": [1.0, 1e-10],
         "fit_intercept": [True],
         "solver, max_iter, tol": [
-            ("svd", None, 0),
-            ("cholesky", None, 0),
-            ("lsqr", 25, 0),
-            ("sparse_cg", 25, 0),
-            ("sag", 50, 0),
-            ("saga", 25, 0),
-            ("lbfgs", 100, 0),
-            # Used for solvers like scikit-learn-intelex that doesn't
+            ("svd", None, 1e-4),
+            ("cholesky", None, 1e-4),
+            ("lsqr", None, 1e-4),
+            ("sparse_cg", None, 1e-4),
+            ("sag", None, 1e-4),
+            ("saga", None, 1e-4),
+            ("lbfgs", None, 1e-4),
+            # Used for scikit-learn-intelex that doesn't
             # document the underlying solver nor expose the n_iter_ attribute
-            ("???", None, 0),
+            ("DefaultDense", None, 1e-4),
         ],
         "sample_weight": ["None"],  # NB: add "random" to test non None weights
         "random_state": [123],
diff --git a/benchmarks/ridge/solvers/scikit_learn.py b/benchmarks/ridge/solvers/scikit_learn.py
index 4a2d278..8a0c76b 100644
--- a/benchmarks/ridge/solvers/scikit_learn.py
+++ b/benchmarks/ridge/solvers/scikit_learn.py
@@ -45,8 +45,8 @@ def skip(self, **objective_dict):
                 "in the benchmark."
             )
 
-        if solver == "???":
-            return True, ""
+        if solver == "DefaultDense":
+            return True, "No support this solver parameter."
 
         return False, None
 
diff --git a/benchmarks/ridge/solvers/scikit_learn_intelex.py b/benchmarks/ridge/solvers/scikit_learn_intelex.py
index 88cb36f..571705a 100644
--- a/benchmarks/ridge/solvers/scikit_learn_intelex.py
+++ b/benchmarks/ridge/solvers/scikit_learn_intelex.py
@@ -49,9 +49,9 @@ def skip(self, **objective_dict):
 
         solver = objective_dict["solver"]
 
-        if solver != "???":
+        if solver != "DefaultDense":
             # TODO: investigate ?
-            return True, "The underlying solver is unknown."
+            return True, "DefaultDense is the only supported solver parameter."
 
         return False, None
 

From bd78618228cbb76897a048e5d20d13afdf899413 Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Wed, 17 Jan 2024 11:16:09 +0100
Subject: [PATCH 33/43] Nits

---
 benchmarks/ridge/objective.py                    | 5 +----
 benchmarks/ridge/solvers/scikit_learn.py         | 2 +-
 benchmarks/ridge/solvers/scikit_learn_intelex.py | 2 +-
 3 files changed, 3 insertions(+), 6 deletions(-)

diff --git a/benchmarks/ridge/objective.py b/benchmarks/ridge/objective.py
index 4681241..0c34844 100644
--- a/benchmarks/ridge/objective.py
+++ b/benchmarks/ridge/objective.py
@@ -95,10 +95,7 @@ def evaluate_result(self, weights, intercept, n_iter, **solver_parameters):
         if not (isinstance(n_iter, numbers.Number) or (n_iter is None)):
             n_iter = set(n_iter)
             if len(n_iter) > 1:
-                raise ValueError(
-                    "In multitarget mode, the same number of iterations is expected "
-                    "for all targets, to keep reports comparable."
-                )
+                n_iter = max(n_iter)
             n_iter = n_iter.pop()
 
         # NB: str for n_iter is a more practical type because it enables
diff --git a/benchmarks/ridge/solvers/scikit_learn.py b/benchmarks/ridge/solvers/scikit_learn.py
index 8a0c76b..971a76d 100644
--- a/benchmarks/ridge/solvers/scikit_learn.py
+++ b/benchmarks/ridge/solvers/scikit_learn.py
@@ -46,7 +46,7 @@ def skip(self, **objective_dict):
             )
 
         if solver == "DefaultDense":
-            return True, "No support this solver parameter."
+            return True, "No support for this solver parameter."
 
         return False, None
 
diff --git a/benchmarks/ridge/solvers/scikit_learn_intelex.py b/benchmarks/ridge/solvers/scikit_learn_intelex.py
index 571705a..02445bb 100644
--- a/benchmarks/ridge/solvers/scikit_learn_intelex.py
+++ b/benchmarks/ridge/solvers/scikit_learn_intelex.py
@@ -51,7 +51,7 @@ def skip(self, **objective_dict):
 
         if solver != "DefaultDense":
             # TODO: investigate ?
-            return True, "DefaultDense is the only supported solver parameter."
+            return True, "The only supported solver parameter is DefaultDense."
 
         return False, None
 

From 35a40d0818f1f67cfb21725f520eb435eb6e1423 Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Wed, 17 Jan 2024 11:55:13 +0100
Subject: [PATCH 34/43] Fix lsqr

---
 benchmarks/ridge/objective.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/benchmarks/ridge/objective.py b/benchmarks/ridge/objective.py
index 0c34844..9f7043e 100644
--- a/benchmarks/ridge/objective.py
+++ b/benchmarks/ridge/objective.py
@@ -93,10 +93,7 @@ def evaluate_result(self, weights, intercept, n_iter, **solver_parameters):
         )
 
         if not (isinstance(n_iter, numbers.Number) or (n_iter is None)):
-            n_iter = set(n_iter)
-            if len(n_iter) > 1:
-                n_iter = max(n_iter)
-            n_iter = n_iter.pop()
+            n_iter = max(n_iter)
 
         # NB: str for n_iter is a more practical type because it enables
         # using missing values for solvers for which it doesn't apply

From 934460286c30eed7add9ad6ca42e0a1361efb78e Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Wed, 17 Jan 2024 13:37:45 +0100
Subject: [PATCH 35/43] Update results (wip: cuml missing)

---
 benchmarks/ridge/consolidate_result_csv.py    |   1 +
 .../benchopt_run_2024-01-15_19h50m23.parquet  | Bin 33882 -> 0 bytes
 .../benchopt_run_2024-01-15_20h06m24.parquet  | Bin 33618 -> 0 bytes
 .../benchopt_run_2024-01-16_14h54m53.parquet  | Bin 34246 -> 0 bytes
 .../benchopt_run_2024-01-16_16h08m16.parquet  | Bin 34664 -> 0 bytes
 .../benchopt_run_2024-01-17_12h14m29.parquet  | Bin 0 -> 36368 bytes
 .../benchopt_run_2024-01-17_13h01m18.parquet  | Bin 0 -> 34214 bytes
 benchmarks/ridge/results.csv                  | 126 ++++++++++--------
 8 files changed, 74 insertions(+), 53 deletions(-)
 delete mode 100644 benchmarks/ridge/outputs/benchopt_run_2024-01-15_19h50m23.parquet
 delete mode 100644 benchmarks/ridge/outputs/benchopt_run_2024-01-15_20h06m24.parquet
 delete mode 100644 benchmarks/ridge/outputs/benchopt_run_2024-01-16_14h54m53.parquet
 delete mode 100644 benchmarks/ridge/outputs/benchopt_run_2024-01-16_16h08m16.parquet
 create mode 100644 benchmarks/ridge/outputs/benchopt_run_2024-01-17_12h14m29.parquet
 create mode 100644 benchmarks/ridge/outputs/benchopt_run_2024-01-17_13h01m18.parquet

diff --git a/benchmarks/ridge/consolidate_result_csv.py b/benchmarks/ridge/consolidate_result_csv.py
index 76cf2de..dc15978 100644
--- a/benchmarks/ridge/consolidate_result_csv.py
+++ b/benchmarks/ridge/consolidate_result_csv.py
@@ -174,6 +174,7 @@
     objective_dataset_param_n_samples=NB_DATA_SAMPLES,
     objective_dataset_param_n_features=NB_DATA_FEATURES,
     objective_dataset_param_n_targets=NB_DATA_TARGETS,
+    objective_objective_param_alpha=REGULARIZATION_STRENGTH,
     objective_dataset_param_dtype=DTYPE,
     objective_dataset_param_random_state=DATA_RANDOM_STATE,
     objective_objective_param_random_state=SOLVER_RANDOM_STATE,
diff --git a/benchmarks/ridge/outputs/benchopt_run_2024-01-15_19h50m23.parquet b/benchmarks/ridge/outputs/benchopt_run_2024-01-15_19h50m23.parquet
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diff --git a/benchmarks/ridge/results.csv b/benchmarks/ridge/results.csv
index 9df6e45..270518c 100644
--- a/benchmarks/ridge/results.csv
+++ b/benchmarks/ridge/results.csv
@@ -1,53 +1,73 @@
-Benchmark id,Dtype,Nb data samples,Nb data features,Nb data targets,Walltime,Backend provider,Compute device,Compute runtime,Solver,Nb cpus,Cpu name,Gpu name,RAM (GB),Platform,Platform architecture,Platform release,Run date,Version info,Comment,Result nb iterations,Result objective value,Data random state,Solver random state
-3a327106,float32,5000000,100,1,0.306,scikit-learn-intelex,cpu,,???,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn-intelex 2024.0.1,,,4.506280786401367,123,123
-3a327106,float32,5000000,100,1,0.735,sklearn-torch-dispatch,cuda,,svd,32,AMD EPYC 7302 16-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,4.506280786413193,123,123
-3a327106,float32,5000000,100,1,1.37,cuml,gpu,,svd,32,AMD EPYC 7502 32-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,4.506280786409378,123,123
-3a327106,float32,5000000,100,1,1.73,scikit-learn,,,cholesky,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,4.506297986368179,123,123
-3a327106,float32,5000000,100,1,4.3,scikit-learn,,,lsqr,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,4.506297986381912,123,123
-3a327106,float32,5000000,100,1,5.56,sklearn-torch-dispatch,cpu,,svd,32,AMD EPYC 7302 16-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,4.506281586742783,123,123
-3a327106,float32,5000000,100,1,13.7,scikit-learn,,,svd,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,4.506297186405945,123,123
-9bd2a256,float32,5000000,100,10,0.26,scikit-learn-intelex,cpu,,???,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn-intelex 2024.0.1,,,2.079804933099976,123,123
-9bd2a256,float32,5000000,100,10,0.74,sklearn-torch-dispatch,cuda,,svd,32,AMD EPYC 7302 16-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,2.0798047731054687,123,123
-9bd2a256,float32,5000000,100,10,1.93,scikit-learn,,,cholesky,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.079808773100586,123,123
-9bd2a256,float32,5000000,100,10,5.78,sklearn-torch-dispatch,cpu,,svd,32,AMD EPYC 7302 16-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,2.0798046132958983,123,123
-9bd2a256,float32,5000000,100,10,14.9,scikit-learn,,,svd,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.079808933101807,123,123
-9bd2a256,float32,5000000,100,10,37.2,scikit-learn,,,lsqr,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,2.07980909309967,123,123
-a9a05f25,float32,5000000,10,1,0.0273,scikit-learn-intelex,cpu,,???,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn-intelex 2024.0.1,,,0.0251269475670009,123,123
-a9a05f25,float32,5000000,10,1,0.0324,sklearn-torch-dispatch,cuda,,svd,32,AMD EPYC 7302 16-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,0.0251269460045129,123,123
-a9a05f25,float32,5000000,10,1,0.131,cuml,gpu,,svd,32,AMD EPYC 7502 32-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,0.0251269475669592,123,123
-a9a05f25,float32,5000000,10,1,0.245,scikit-learn,,,cholesky,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,0.02512695694184,123,123
-a9a05f25,float32,5000000,10,1,0.527,sklearn-torch-dispatch,cpu,,svd,32,AMD EPYC 7302 16-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,0.0251269460045427,123,123
-a9a05f25,float32,5000000,10,1,0.796,scikit-learn,,,svd,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,0.025126958504486,123,123
-a9a05f25,float32,5000000,10,1,0.842,scikit-learn,,,lsqr,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,0.025126955379498,123,123
-ffd32c2e,float32,5000000,10,10,0.0375,sklearn-torch-dispatch,cuda,,svd,32,AMD EPYC 7302 16-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,0.1351635653306007,123,123
-ffd32c2e,float32,5000000,10,10,0.0458,scikit-learn-intelex,cpu,,???,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn-intelex 2024.0.1,,,0.1351635853306198,123,123
-ffd32c2e,float32,5000000,10,10,0.441,scikit-learn,,,cholesky,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,0.1351636653292465,123,123
-ffd32c2e,float32,5000000,10,10,0.688,sklearn-torch-dispatch,cpu,,svd,32,AMD EPYC 7302 16-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,0.1351635653207397,123,123
-ffd32c2e,float32,5000000,10,10,1.01,scikit-learn,,,svd,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,0.1351636653303528,123,123
-ffd32c2e,float32,5000000,10,10,7.28,scikit-learn,,,lsqr,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,0.1351636553305053,123,123
-c49c3df8,float32,2000000,100,1,0.0924,scikit-learn-intelex,cpu,,???,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn-intelex 2024.0.1,,,2.283151841181755,123,123
-c49c3df8,float32,2000000,100,1,0.283,sklearn-torch-dispatch,cuda,,svd,32,AMD EPYC 7302 16-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,2.28315184118557,123,123
-c49c3df8,float32,2000000,100,1,0.557,cuml,gpu,,svd,32,AMD EPYC 7502 32-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.283151841176033,123,123
-c49c3df8,float32,2000000,100,1,0.727,scikit-learn,,,cholesky,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.283155591078758,123,123
-c49c3df8,float32,2000000,100,1,1.84,scikit-learn,,,lsqr,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,2.283155091144562,123,123
-c49c3df8,float32,2000000,100,1,2.35,sklearn-torch-dispatch,cpu,,svd,32,AMD EPYC 7302 16-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,2.2831518412952425,123,123
-c49c3df8,float32,2000000,100,1,5.62,scikit-learn,,,svd,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.2831550911808014,123,123
-1a8c391a,float32,2000000,100,10,0.0961,scikit-learn-intelex,cpu,,???,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn-intelex 2024.0.1,,,2.160555392794037,123,123
-1a8c391a,float32,2000000,100,10,0.282,sklearn-torch-dispatch,cuda,,svd,32,AMD EPYC 7302 16-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,2.160555392803192,123,123
-1a8c391a,float32,2000000,100,10,0.779,scikit-learn,,,cholesky,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.160573592800904,123,123
-1a8c391a,float32,2000000,100,10,2.61,sklearn-torch-dispatch,cpu,,svd,32,AMD EPYC 7302 16-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,2.16055519289856,123,123
-1a8c391a,float32,2000000,100,10,5.7,scikit-learn,,,svd,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.1605733927993773,123,123
-1a8c391a,float32,2000000,100,10,16.4,scikit-learn,,,lsqr,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,2.1605733928001403,123,123
-e5684553,float32,5000,5000,1,0.166,scikit-learn,,,lsqr,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,9.347395458984376,123,123
-e5684553,float32,5000,5000,1,0.541,scikit-learn,,,cholesky,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.45976669921875,123,123
-e5684553,float32,5000,5000,1,0.557,scikit-learn-intelex,cpu,,???,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn-intelex 2024.0.1,,,3.76711484375,123,123
-e5684553,float32,5000,5000,1,6.29,cuml,gpu,,svd,32,AMD EPYC 7502 32-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.20544453125,123,123
-e5684553,float32,5000,5000,1,7.03,sklearn-torch-dispatch,cpu,,svd,32,AMD EPYC 7302 16-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,3.76734375,123,123
-e5684553,float32,5000,5000,1,9.08,scikit-learn,,,svd,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.459914453125,123,123
-e5684553,float32,5000,5000,1,10.7,sklearn-torch-dispatch,cuda,,svd,32,AMD EPYC 7302 16-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,3.76877265625,123,123
-7b05f2bf,float32,5000,5000,10,0.478,scikit-learn,,,cholesky,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.781028203125,123,123
-7b05f2bf,float32,5000,5000,10,0.532,scikit-learn-intelex,cpu,,???,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn-intelex 2024.0.1,,,2.74752203125,123,123
-7b05f2bf,float32,5000,5000,10,1.22,scikit-learn,,,lsqr,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,25,9.960193984375,123,123
-7b05f2bf,float32,5000,5000,10,6.82,sklearn-torch-dispatch,cpu,,svd,32,AMD EPYC 7302 16-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,2.747505859375,123,123
-7b05f2bf,float32,5000,5000,10,9.23,scikit-learn,,,svd,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-15,scikit-learn 1.3.2,,,2.781131640625,123,123
-7b05f2bf,float32,5000,5000,10,10.7,sklearn-torch-dispatch,cuda,,svd,32,AMD EPYC 7302 16-Core Processor,NVIDIA A100-PCIE-40GB,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-16,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,2.747708828125,123,123
+Benchmark id,Dtype,Nb data samples,Nb data features,Nb data targets,Regularization strength,Walltime,Backend provider,Compute device,Compute runtime,Solver,Nb cpus,Cpu name,Gpu name,RAM (GB),Platform,Platform architecture,Platform release,Run date,Version info,Comment,Result nb iterations,Result objective value,Data random state,Solver random state
+cbe96b03,float32,10000000,100,1,1.0,0.563,scikit-learn-intelex,cpu,,DefaultDense,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn-intelex 2024.0.1,,,3.105119922031021,123,123
+cbe96b03,float32,10000000,100,1,1.0,0.79,sklearn-torch-dispatch,cuda,,svd,32,AMD EPYC 7402 24-Core Processor,NVIDIA A100-PCIE-40GB,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,3.10511992203598,123,123
+cbe96b03,float32,10000000,100,1,1.0,3.42,scikit-learn,,,cholesky,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,,3.1051197220314024,123,123
+cbe96b03,float32,10000000,100,1,1.0,5.96,scikit-learn,,,lsqr,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,17,3.105119122049713,123,123
+cbe96b03,float32,10000000,100,1,1.0,8.91,sklearn-torch-dispatch,cpu,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,3.1051209226127625,123,123
+cbe96b03,float32,10000000,100,1,1.0,52.7,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,,3.105120122029496,123,123
+f976a4f8,float32,10000000,100,1,1e-10,0.408,scikit-learn-intelex,cpu,,DefaultDense,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn-intelex 2024.0.1,,,3.1051164000000004,123,123
+f976a4f8,float32,10000000,100,1,1e-10,0.742,sklearn-torch-dispatch,cuda,,svd,32,AMD EPYC 7402 24-Core Processor,NVIDIA A100-PCIE-40GB,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,3.1051164000000004,123,123
+f976a4f8,float32,10000000,100,1,1e-10,3.45,scikit-learn,,,cholesky,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,,3.1051164000000004,123,123
+f976a4f8,float32,10000000,100,1,1e-10,6.14,scikit-learn,,,lsqr,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,17,3.1051158000000005,123,123
+f976a4f8,float32,10000000,100,1,1e-10,9.2,sklearn-torch-dispatch,cpu,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,3.1051176000000003,123,123
+f976a4f8,float32,10000000,100,1,1e-10,52.3,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,,3.1051168,123,123
+a9625843,float32,10000000,100,10,1.0,0.452,scikit-learn-intelex,cpu,,DefaultDense,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn-intelex 2024.0.1,,,2.692140948616333,123,123
+a9625843,float32,10000000,100,10,1.0,0.75,sklearn-torch-dispatch,cuda,,svd,32,AMD EPYC 7402 24-Core Processor,NVIDIA A100-PCIE-40GB,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,2.692140628620148,123,123
+a9625843,float32,10000000,100,10,1.0,3.94,scikit-learn,,,cholesky,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,,2.692140308622131,123,123
+a9625843,float32,10000000,100,10,1.0,9.93,sklearn-torch-dispatch,cpu,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,2.6921425489894104,123,123
+a9625843,float32,10000000,100,10,1.0,48.9,scikit-learn,,,lsqr,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,18,2.6921428686135864,123,123
+a9625843,float32,10000000,100,10,1.0,52.8,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,,2.69214062861969,123,123
+8ed75954,float32,10000000,100,10,1e-10,0.44,scikit-learn-intelex,cpu,,DefaultDense,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn-intelex 2024.0.1,,,2.69213856,123,123
+8ed75954,float32,10000000,100,10,1e-10,0.749,sklearn-torch-dispatch,cuda,,svd,32,AMD EPYC 7402 24-Core Processor,NVIDIA A100-PCIE-40GB,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,2.69213824,123,123
+8ed75954,float32,10000000,100,10,1e-10,3.89,scikit-learn,,,cholesky,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,,2.69213792,123,123
+8ed75954,float32,10000000,100,10,1e-10,9.78,sklearn-torch-dispatch,cpu,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,2.69214016,123,123
+8ed75954,float32,10000000,100,10,1e-10,48.6,scikit-learn,,,lsqr,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,18,2.69214048,123,123
+8ed75954,float32,10000000,100,10,1e-10,51.6,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,,2.69213824,123,123
+67d027a4,float32,10000000,10,1,1.0,0.0439,scikit-learn-intelex,cpu,,DefaultDense,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn-intelex 2024.0.1,,,0.5324485410551071,123,123
+67d027a4,float32,10000000,10,1,1.0,0.0531,sklearn-torch-dispatch,cuda,,svd,32,AMD EPYC 7402 24-Core Processor,NVIDIA A100-PCIE-40GB,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,0.5324485410553932,123,123
+67d027a4,float32,10000000,10,1,1.0,0.46,scikit-learn,,,cholesky,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,,0.5324485410561561,123,123
+67d027a4,float32,10000000,10,1,1.0,0.834,scikit-learn,,,lsqr,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,10,0.5324489910561084,123,123
+67d027a4,float32,10000000,10,1,1.0,0.86,sklearn-torch-dispatch,cpu,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,0.5324485410661698,123,123
+67d027a4,float32,10000000,10,1,1.0,3,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,,0.5324485910551549,123,123
+4fe92f89,float32,10000000,10,1,1e-10,0.038,sklearn-torch-dispatch,cuda,,svd,32,AMD EPYC 7402 24-Core Processor,NVIDIA A100-PCIE-40GB,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,0.5324481,123,123
+4fe92f89,float32,10000000,10,1,1e-10,0.0495,scikit-learn-intelex,cpu,,DefaultDense,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn-intelex 2024.0.1,,,0.53244815,123,123
+4fe92f89,float32,10000000,10,1,1e-10,0.461,scikit-learn,,,cholesky,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,,0.53244815,123,123
+4fe92f89,float32,10000000,10,1,1e-10,0.847,scikit-learn,,,lsqr,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,10,0.53244855,123,123
+4fe92f89,float32,10000000,10,1,1e-10,0.855,sklearn-torch-dispatch,cpu,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,0.5324481,123,123
+4fe92f89,float32,10000000,10,1,1e-10,2.97,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,,0.53244815,123,123
+9355a1d7,float32,10000000,10,10,1.0,0.0585,sklearn-torch-dispatch,cuda,,svd,32,AMD EPYC 7402 24-Core Processor,NVIDIA A100-PCIE-40GB,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,0.2334089881958007,123,123
+9355a1d7,float32,10000000,10,10,1.0,0.0745,scikit-learn-intelex,cpu,,DefaultDense,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn-intelex 2024.0.1,,,0.2334089881957817,123,123
+9355a1d7,float32,10000000,10,10,1.0,0.901,scikit-learn,,,cholesky,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,,0.2334089681967163,123,123
+9355a1d7,float32,10000000,10,10,1.0,1.13,sklearn-torch-dispatch,cpu,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,0.2334098480794334,123,123
+9355a1d7,float32,10000000,10,10,1.0,3.62,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,,0.2334089881959343,123,123
+9355a1d7,float32,10000000,10,10,1.0,6.36,scikit-learn,,,lsqr,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,11,0.2334089881908989,123,123
+a14c1361,float32,10000000,10,10,1e-10,0.04,sklearn-torch-dispatch,cuda,,svd,32,AMD EPYC 7402 24-Core Processor,NVIDIA A100-PCIE-40GB,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,0.23340876,123,123
+a14c1361,float32,10000000,10,10,1e-10,0.0739,scikit-learn-intelex,cpu,,DefaultDense,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn-intelex 2024.0.1,,,0.23340876,123,123
+a14c1361,float32,10000000,10,10,1e-10,0.908,scikit-learn,,,cholesky,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,,0.23340874,123,123
+a14c1361,float32,10000000,10,10,1e-10,1.13,sklearn-torch-dispatch,cpu,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,0.23340962,123,123
+a14c1361,float32,10000000,10,10,1e-10,3.43,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,,0.23340876,123,123
+a14c1361,float32,10000000,10,10,1e-10,6.26,scikit-learn,,,lsqr,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,11,0.23340876,123,123
+73b8dab5,float32,5000,10000,1,1.0,0.576,scikit-learn,,,cholesky,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,,0.4365529907226562,123,123
+73b8dab5,float32,5000,10000,1,1.0,0.593,scikit-learn-intelex,cpu,,DefaultDense,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn-intelex 2024.0.1,,,0.4365529907226562,123,123
+73b8dab5,float32,5000,10000,1,1.0,0.668,scikit-learn,,,lsqr,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,82,0.4365451538085937,123,123
+73b8dab5,float32,5000,10000,1,1.0,3.66,sklearn-torch-dispatch,cuda,,svd,32,AMD EPYC 7402 24-Core Processor,NVIDIA A100-PCIE-40GB,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,0.4365692749023437,123,123
+73b8dab5,float32,5000,10000,1,1.0,11.9,sklearn-torch-dispatch,cpu,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,0.4365479858398438,123,123
+73b8dab5,float32,5000,10000,1,1.0,24.1,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,,0.4365515014648437,123,123
+1e6fef9b,float32,5000,10000,1,1e-10,0.591,scikit-learn,,,lsqr,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,64,0.1466086914354196,123,123
+1e6fef9b,float32,5000,10000,1,1e-10,3.55,sklearn-torch-dispatch,cuda,,svd,32,AMD EPYC 7402 24-Core Processor,NVIDIA A100-PCIE-40GB,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,0.1557751831353653,123,123
+1e6fef9b,float32,5000,10000,1,1e-10,5.79,scikit-learn,,,cholesky,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,,0.1377344848931573,123,123
+1e6fef9b,float32,5000,10000,1,1e-10,5.79,scikit-learn-intelex,cpu,,DefaultDense,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn-intelex 2024.0.1,,,0.1377344848931573,123,123
+1e6fef9b,float32,5000,10000,1,1e-10,12,sklearn-torch-dispatch,cpu,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,0.128667602568943,123,123
+1e6fef9b,float32,5000,10000,1,1e-10,23.4,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,,0.1380022217095494,123,123
+06511952,float32,5000,10000,10,1.0,0.576,scikit-learn,,,cholesky,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,,0.9041459375,123,123
+06511952,float32,5000,10000,10,1.0,0.583,scikit-learn-intelex,cpu,,DefaultDense,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn-intelex 2024.0.1,,,0.9041459375,123,123
+06511952,float32,5000,10000,10,1.0,3.61,sklearn-torch-dispatch,cuda,,svd,32,AMD EPYC 7402 24-Core Processor,NVIDIA A100-PCIE-40GB,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,0.9043073828125,123,123
+06511952,float32,5000,10000,10,1.0,6.45,scikit-learn,,,lsqr,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,82,0.90424390625,123,123
+06511952,float32,5000,10000,10,1.0,12,sklearn-torch-dispatch,cpu,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,0.904237265625,123,123
+06511952,float32,5000,10000,10,1.0,24.2,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,,0.904249765625,123,123
+6e8bfb2d,float32,5000,10000,10,1e-10,3.56,sklearn-torch-dispatch,cuda,,svd,32,AMD EPYC 7402 24-Core Processor,NVIDIA A100-PCIE-40GB,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,0.4814392579111228,123,123
+6e8bfb2d,float32,5000,10000,10,1e-10,4.68,scikit-learn,,,lsqr,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,64,0.3175425000945574,123,123
+6e8bfb2d,float32,5000,10000,10,1e-10,6.2,scikit-learn-intelex,cpu,,DefaultDense,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn-intelex 2024.0.1,,,0.3010748829093572,123,123
+6e8bfb2d,float32,5000,10000,10,1e-10,6.31,scikit-learn,,,cholesky,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,,0.3010748829093572,123,123
+6e8bfb2d,float32,5000,10000,10,1e-10,11.7,sklearn-torch-dispatch,cpu,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,0.2938720313465286,123,123
+6e8bfb2d,float32,5000,10000,10,1e-10,23.9,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,,0.3019234375965278,123,123

From 6d021d073c471aa936ab1b1301b89e7dd16d9fec Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Wed, 17 Jan 2024 15:27:16 +0100
Subject: [PATCH 36/43] revert unrelated kmeans changes

---
 benchmarks/kmeans/objective.py | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/benchmarks/kmeans/objective.py b/benchmarks/kmeans/objective.py
index 942f701..63f3bf1 100644
--- a/benchmarks/kmeans/objective.py
+++ b/benchmarks/kmeans/objective.py
@@ -32,15 +32,15 @@ def set_data(self, X, **dataset_parameters):
         self.X = X
         dtype = X.dtype
 
+        if self.init == "random" or self.sample_weight == "random":
+            rng = np.random.default_rng(self.random_state)
+
         if self.sample_weight == "None":
             sample_weight = None
         elif self.sample_weight == "unary":
             sample_weight = np.ones(len(X), dtype=dtype)
         elif self.sample_weight == "random":
-            rng_sample_weight = np.random.default_rng(
-                dataset_parameters["random_state"] + 1
-            )
-            sample_weight = rng_sample_weight.random(size=len(X)).astype(dtype)
+            sample_weight = rng.random(size=len(X)).astype(dtype)
         else:
             raise ValueError(
                 "Expected 'sample_weight' parameter to be either equal to 'None', "
@@ -48,9 +48,8 @@ def set_data(self, X, **dataset_parameters):
             )
 
         if self.init == "random":
-            rng_init = np.random.default_rng(self.random_state)
             init = np.array(
-                rng_init.choice(X, self.n_clusters, replace=False), dtype=X.dtype
+                rng.choice(X, self.n_clusters, replace=False), dtype=X.dtype
             )
         elif self.init == "k-means++":
             init = self.init

From e7a1399f3b5e28ac72c9d1dc27dc3d4a3e24a25e Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Thu, 18 Jan 2024 11:10:11 +0100
Subject: [PATCH 37/43] Add more solvers and dimensions, work around issues
 with incompatible dimensions in warmup

---
 benchmarks/ridge/datasets/simulated_blobs.py  |  4 +-
 benchmarks/ridge/objective.py                 |  6 +-
 benchmarks/ridge/solvers/cuml.py              | 56 ++++++++++--------
 benchmarks/ridge/solvers/scikit_learn.py      | 14 +++--
 .../ridge/solvers/scikit_learn_intelex.py     | 58 ++++++++++---------
 .../ridge/solvers/sklearn_torch_dispatch.py   | 54 +++++++++--------
 6 files changed, 110 insertions(+), 82 deletions(-)

diff --git a/benchmarks/ridge/datasets/simulated_blobs.py b/benchmarks/ridge/datasets/simulated_blobs.py
index 5d3276a..d850f73 100644
--- a/benchmarks/ridge/datasets/simulated_blobs.py
+++ b/benchmarks/ridge/datasets/simulated_blobs.py
@@ -11,8 +11,10 @@ class Dataset(BaseDataset):
     parameters = {
         "n_samples, n_features": [
             (10_000_000, 100),
-            (10_000_000, 10),
+            (5_000_000, 100),
+            (5_000_000, 10),
             (5000, 10000),
+            (5000, 5000),
         ],
         "n_targets": [1, 10],
         "dtype": ["float32"],
diff --git a/benchmarks/ridge/objective.py b/benchmarks/ridge/objective.py
index 9f7043e..3dbfcdb 100644
--- a/benchmarks/ridge/objective.py
+++ b/benchmarks/ridge/objective.py
@@ -22,13 +22,15 @@ class Objective(BaseObjective):
         "alpha": [1.0, 1e-10],
         "fit_intercept": [True],
         "solver, max_iter, tol": [
-            ("svd", None, 1e-4),
-            ("cholesky", None, 1e-4),
+            ("svd", None, 0),
+            ("cholesky", None, 0),
             ("lsqr", None, 1e-4),
             ("sparse_cg", None, 1e-4),
             ("sag", None, 1e-4),
             ("saga", None, 1e-4),
             ("lbfgs", None, 1e-4),
+            ("cg", None, 1e-4),
+            ("eig", None, None),
             # Used for scikit-learn-intelex that doesn't
             # document the underlying solver nor expose the n_iter_ attribute
             ("DefaultDense", None, 1e-4),
diff --git a/benchmarks/ridge/solvers/cuml.py b/benchmarks/ridge/solvers/cuml.py
index c64cd41..6245868 100644
--- a/benchmarks/ridge/solvers/cuml.py
+++ b/benchmarks/ridge/solvers/cuml.py
@@ -19,29 +19,6 @@ class Solver(BaseSolver):
 
     stopping_criterion = SingleRunCriterion(1)
 
-    def skip(self, **objective_dict):
-
-        X = objective_dict["X"]
-        if X.dtype == np.float64:
-            # We haven't came accross cuda devices that doesn't support float64 yet,
-            # can it happen ? If it happens, the following instruction will fail,
-            # please enclose it with the appropriate Try/Except to return the
-            # appropriate skip decision.
-            cupy.zeros(1, dtype=cupy.float64)
-            # return True, (
-            #     f"This {self.device} device has no support for float64 compute"
-            # )
-
-        y = objective_dict["y"]
-        if (y.ndim == 2) and (y.shape[1] > 1):
-            return True, "Multitarget is not supported."
-
-        solver = objective_dict["solver"]
-        if solver != "svd":
-            return True, "Only accepts the svd solver at the moment."
-
-        return False, None
-
     def set_objective(
         self,
         X,
@@ -68,15 +45,44 @@ def set_objective(
         self.tol = tol
         self.random_state = random_state
 
+    def skip(self, **objective_dict):
+
+        X = objective_dict["X"]
+        if X.dtype == np.float64:
+            # We haven't came accross cuda devices that doesn't support float64 yet,
+            # can it happen ? If it happens, the following instruction will fail,
+            # please enclose it with the appropriate Try/Except to return the
+            # appropriate skip decision.
+            cupy.zeros(1, dtype=cupy.float64)
+            # return True, (
+            #     f"This {self.device} device has no support for float64 compute"
+            # )
+
+        y = objective_dict["y"]
+        if (y.ndim == 2) and (y.shape[1] > 1):
+            return True, "Multitarget is not supported."
+
+        solver = objective_dict["solver"]
+        if solver not in ["svd", "cg", "eig"]:
+            return True, "Only accepts the svd solver at the moment."
+
+        return False, None
+
     def warm_up(self):
+        n_warmup_samples = 20
+        n_warmup_features = 5
         sample_weight = self.sample_weight
         if sample_weight is not None:
-            sample_weight = sample_weight[:2]
+            sample_weight = sample_weight[:n_warmup_samples]
         cuml.Ridge(
             alpha=self.alpha,
             fit_intercept=self.fit_intercept,
             solver=self.solver,
-        ).fit(self.X[:2], self.y[:2], sample_weight=sample_weight)
+        ).fit(
+            self.X[:n_warmup_samples, :n_warmup_features],
+            self.y[:n_warmup_samples],
+            sample_weight=sample_weight,
+        )
 
     def run(self, _):
         estimator = cuml.Ridge(
diff --git a/benchmarks/ridge/solvers/scikit_learn.py b/benchmarks/ridge/solvers/scikit_learn.py
index 971a76d..dec7041 100644
--- a/benchmarks/ridge/solvers/scikit_learn.py
+++ b/benchmarks/ridge/solvers/scikit_learn.py
@@ -38,22 +38,24 @@ def set_objective(
     def skip(self, **objective_dict):
         solver = objective_dict["solver"]
 
-        if solver in ["sag", "saga", "sparse_cg", "lbfgs"]:
+        if solver in ["sag", "saga"]:
             # TODO: investigate ?
             return True, (
                 "Preliminary testing show this solver is too slow to have relevance "
                 "in the benchmark."
             )
 
-        if solver == "DefaultDense":
+        if solver in ["DefaultDense", "eig", "cg"]:
             return True, "No support for this solver parameter."
 
         return False, None
 
     def warm_up(self):
+        n_warmup_samples = 20
+        n_warmup_features = 5
         sample_weight = self.sample_weight
         if sample_weight is not None:
-            sample_weight = sample_weight[:2]
+            sample_weight = sample_weight[:20]
         Ridge(
             alpha=self.alpha,
             fit_intercept=self.fit_intercept,
@@ -63,7 +65,11 @@ def warm_up(self):
             solver=self.solver,
             positive=True if (self.solver == "lbfgs") else False,
             random_state=self.random_state,
-        ).fit(self.X[:2], self.y[:2], sample_weight)
+        ).fit(
+            self.X[:n_warmup_samples, :n_warmup_features],
+            self.y[:n_warmup_samples],
+            sample_weight,
+        )
 
     def run(self, _):
         estimator = Ridge(
diff --git a/benchmarks/ridge/solvers/scikit_learn_intelex.py b/benchmarks/ridge/solvers/scikit_learn_intelex.py
index 02445bb..29cca65 100644
--- a/benchmarks/ridge/solvers/scikit_learn_intelex.py
+++ b/benchmarks/ridge/solvers/scikit_learn_intelex.py
@@ -31,30 +31,6 @@ class Solver(BaseSolver):
 
     stopping_criterion = SingleRunCriterion(1)
 
-    def skip(self, **objective_dict):
-        if self.runtime is not None:
-            try:
-                device = dpctl.SyclDevice(f"{self.runtime}:{self.device}")
-            except Exception:
-                return (
-                    True,
-                    f"{self.runtime} runtime not found for device {self.device}",
-                )
-
-            X = objective_dict["X"]
-            if (X.dtype == np.float64) and not device.has_aspect_fp64:
-                return True, (
-                    f"This {self.device} device has no support for float64 compute"
-                )
-
-        solver = objective_dict["solver"]
-
-        if solver != "DefaultDense":
-            # TODO: investigate ?
-            return True, "The only supported solver parameter is DefaultDense."
-
-        return False, None
-
     def set_objective(
         self,
         X,
@@ -94,10 +70,36 @@ def set_objective(
         self.tol = tol
         self.random_state = random_state
 
+    def skip(self, **objective_dict):
+        if self.runtime is not None:
+            try:
+                device = dpctl.SyclDevice(f"{self.runtime}:{self.device}")
+            except Exception:
+                return (
+                    True,
+                    f"{self.runtime} runtime not found for device {self.device}",
+                )
+
+            X = objective_dict["X"]
+            if (X.dtype == np.float64) and not device.has_aspect_fp64:
+                return True, (
+                    f"This {self.device} device has no support for float64 compute"
+                )
+
+        solver = objective_dict["solver"]
+
+        if solver != "DefaultDense":
+            # TODO: investigate ?
+            return True, "The only supported solver parameter is DefaultDense."
+
+        return False, None
+
     def warm_up(self):
+        n_warmup_samples = 20
+        n_warmup_features = 5
         sample_weight = self.sample_weight
         if sample_weight is not None:
-            sample_weight = sample_weight[:2]
+            sample_weight = sample_weight[:n_warmup_samples]
         with nullcontext() if (self.runtime is None) else config_context(
             target_offload=f"{self.runtime}:{self.device}"
         ):
@@ -110,7 +112,11 @@ def warm_up(self):
                 solver="auto",
                 positive=True if (self.solver == "lbfgs") else False,
                 random_state=self.random_state,
-            ).fit(self.X[:2], self.y[:2], sample_weight)
+            ).fit(
+                self.X[:n_warmup_samples, :n_warmup_features],
+                self.y[:n_warmup_samples],
+                sample_weight,
+            )
 
     def run(self, _):
         with nullcontext() if (self.runtime is None) else config_context(
diff --git a/benchmarks/ridge/solvers/sklearn_torch_dispatch.py b/benchmarks/ridge/solvers/sklearn_torch_dispatch.py
index c1e4553..26f3194 100644
--- a/benchmarks/ridge/solvers/sklearn_torch_dispatch.py
+++ b/benchmarks/ridge/solvers/sklearn_torch_dispatch.py
@@ -30,29 +30,6 @@ class Solver(BaseSolver):
 
     stopping_criterion = SingleRunCriterion(1)
 
-    def skip(self, **objective_dict):
-        if not Ridge()._get_tags()["array_api_support"]:
-            return True, (
-                "Requires the development branch for Ridge support for Array API."
-            )
-
-        try:
-            torch.zeros(1, dtype=torch.float32, device=self.device)
-        except Exception:
-            return True, f"{self.device} compute backend for pytorch not found"
-
-        X = objective_dict["X"]
-        if (X.dtype == np.float64) and not has_fp64_support(self.device):
-            return True, (
-                f"This {self.device} device has no support for float64 compute"
-            )
-
-        solver = objective_dict["solver"]
-        if solver != "svd":
-            return True, "Only accepts the svd solver at the moment."
-
-        return False, None
-
     def set_objective(
         self,
         X,
@@ -82,7 +59,32 @@ def set_objective(
         self.tol = tol
         self.random_state = random_state
 
+    def skip(self, **objective_dict):
+        if not Ridge()._get_tags()["array_api_support"]:
+            return True, (
+                "Requires the development branch for Ridge support for Array API."
+            )
+
+        try:
+            torch.zeros(1, dtype=torch.float32, device=self.device)
+        except Exception:
+            return True, f"{self.device} compute backend for pytorch not found"
+
+        X = objective_dict["X"]
+        if (X.dtype == np.float64) and not has_fp64_support(self.device):
+            return True, (
+                f"This {self.device} device has no support for float64 compute"
+            )
+
+        solver = objective_dict["solver"]
+        if solver != "svd":
+            return True, "Only accepts the svd solver at the moment."
+
+        return False, None
+
     def warm_up(self):
+        n_warmup_samples = 20
+        n_warmup_features = 5
         sample_weight = self.sample_weight
         if sample_weight is not None:
             sample_weight = sample_weight[:2]
@@ -96,7 +98,11 @@ def warm_up(self):
                 solver=self.solver,
                 positive=True if (self.solver == "lbfgs") else False,
                 random_state=self.random_state,
-            ).fit(self.X[:2], self.y[:2], sample_weight)
+            ).fit(
+                self.X[:n_warmup_samples, :n_warmup_features],
+                self.y[:n_warmup_samples],
+                sample_weight,
+            )
 
     def run(self, _):
         with config_context(array_api_dispatch=True):

From 8144d23493799d3d981c84d79df653fe0139edb9 Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Thu, 18 Jan 2024 11:19:03 +0100
Subject: [PATCH 38/43] fixup

---
 benchmarks/ridge/objective.py                      | 2 +-
 benchmarks/ridge/solvers/cuml.py                   | 8 ++++----
 benchmarks/ridge/solvers/scikit_learn.py           | 8 ++++----
 benchmarks/ridge/solvers/scikit_learn_intelex.py   | 6 +++---
 benchmarks/ridge/solvers/sklearn_torch_dispatch.py | 6 +++---
 5 files changed, 15 insertions(+), 15 deletions(-)

diff --git a/benchmarks/ridge/objective.py b/benchmarks/ridge/objective.py
index 3dbfcdb..88c939e 100644
--- a/benchmarks/ridge/objective.py
+++ b/benchmarks/ridge/objective.py
@@ -29,7 +29,7 @@ class Objective(BaseObjective):
             ("sag", None, 1e-4),
             ("saga", None, 1e-4),
             ("lbfgs", None, 1e-4),
-            ("cg", None, 1e-4),
+            ("cd", None, 1e-4),
             ("eig", None, None),
             # Used for scikit-learn-intelex that doesn't
             # document the underlying solver nor expose the n_iter_ attribute
diff --git a/benchmarks/ridge/solvers/cuml.py b/benchmarks/ridge/solvers/cuml.py
index 6245868..5df29dd 100644
--- a/benchmarks/ridge/solvers/cuml.py
+++ b/benchmarks/ridge/solvers/cuml.py
@@ -63,7 +63,7 @@ def skip(self, **objective_dict):
             return True, "Multitarget is not supported."
 
         solver = objective_dict["solver"]
-        if solver not in ["svd", "cg", "eig"]:
+        if solver not in ["svd", "cd", "eig"]:
             return True, "Only accepts the svd solver at the moment."
 
         return False, None
@@ -73,14 +73,14 @@ def warm_up(self):
         n_warmup_features = 5
         sample_weight = self.sample_weight
         if sample_weight is not None:
-            sample_weight = sample_weight[:n_warmup_samples]
+            sample_weight = (sample_weight[:n_warmup_samples].copy(),)
         cuml.Ridge(
             alpha=self.alpha,
             fit_intercept=self.fit_intercept,
             solver=self.solver,
         ).fit(
-            self.X[:n_warmup_samples, :n_warmup_features],
-            self.y[:n_warmup_samples],
+            self.X[:n_warmup_samples, :n_warmup_features].copy(),
+            self.y[:n_warmup_samples].copy(),
             sample_weight=sample_weight,
         )
 
diff --git a/benchmarks/ridge/solvers/scikit_learn.py b/benchmarks/ridge/solvers/scikit_learn.py
index dec7041..14674a2 100644
--- a/benchmarks/ridge/solvers/scikit_learn.py
+++ b/benchmarks/ridge/solvers/scikit_learn.py
@@ -45,7 +45,7 @@ def skip(self, **objective_dict):
                 "in the benchmark."
             )
 
-        if solver in ["DefaultDense", "eig", "cg"]:
+        if solver in ["DefaultDense", "eig", "cd"]:
             return True, "No support for this solver parameter."
 
         return False, None
@@ -55,7 +55,7 @@ def warm_up(self):
         n_warmup_features = 5
         sample_weight = self.sample_weight
         if sample_weight is not None:
-            sample_weight = sample_weight[:20]
+            sample_weight = sample_weight[:n_warmup_samples].copy()
         Ridge(
             alpha=self.alpha,
             fit_intercept=self.fit_intercept,
@@ -66,8 +66,8 @@ def warm_up(self):
             positive=True if (self.solver == "lbfgs") else False,
             random_state=self.random_state,
         ).fit(
-            self.X[:n_warmup_samples, :n_warmup_features],
-            self.y[:n_warmup_samples],
+            self.X[:n_warmup_samples, :n_warmup_features].copy(),
+            self.y[:n_warmup_samples].copy(),
             sample_weight,
         )
 
diff --git a/benchmarks/ridge/solvers/scikit_learn_intelex.py b/benchmarks/ridge/solvers/scikit_learn_intelex.py
index 29cca65..c5f697f 100644
--- a/benchmarks/ridge/solvers/scikit_learn_intelex.py
+++ b/benchmarks/ridge/solvers/scikit_learn_intelex.py
@@ -99,7 +99,7 @@ def warm_up(self):
         n_warmup_features = 5
         sample_weight = self.sample_weight
         if sample_weight is not None:
-            sample_weight = sample_weight[:n_warmup_samples]
+            sample_weight = sample_weight[:n_warmup_samples].copy()
         with nullcontext() if (self.runtime is None) else config_context(
             target_offload=f"{self.runtime}:{self.device}"
         ):
@@ -113,8 +113,8 @@ def warm_up(self):
                 positive=True if (self.solver == "lbfgs") else False,
                 random_state=self.random_state,
             ).fit(
-                self.X[:n_warmup_samples, :n_warmup_features],
-                self.y[:n_warmup_samples],
+                self.X[:n_warmup_samples, :n_warmup_features].copy(),
+                self.y[:n_warmup_samples].copy(),
                 sample_weight,
             )
 
diff --git a/benchmarks/ridge/solvers/sklearn_torch_dispatch.py b/benchmarks/ridge/solvers/sklearn_torch_dispatch.py
index 26f3194..f51f829 100644
--- a/benchmarks/ridge/solvers/sklearn_torch_dispatch.py
+++ b/benchmarks/ridge/solvers/sklearn_torch_dispatch.py
@@ -87,7 +87,7 @@ def warm_up(self):
         n_warmup_features = 5
         sample_weight = self.sample_weight
         if sample_weight is not None:
-            sample_weight = sample_weight[:2]
+            sample_weight = sample_weight[:n_warmup_samples].copy()
         with config_context(array_api_dispatch=True):
             Ridge(
                 alpha=self.alpha,
@@ -99,8 +99,8 @@ def warm_up(self):
                 positive=True if (self.solver == "lbfgs") else False,
                 random_state=self.random_state,
             ).fit(
-                self.X[:n_warmup_samples, :n_warmup_features],
-                self.y[:n_warmup_samples],
+                self.X[:n_warmup_samples, :n_warmup_features].copy(),
+                self.y[:n_warmup_samples].copy(),
                 sample_weight,
             )
 

From d5bab3fec1f6f48754b5feb5389afe52613fd17c Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Thu, 18 Jan 2024 11:24:48 +0100
Subject: [PATCH 39/43] fixup cuml

---
 benchmarks/ridge/solvers/cuml.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/benchmarks/ridge/solvers/cuml.py b/benchmarks/ridge/solvers/cuml.py
index 5df29dd..b892e96 100644
--- a/benchmarks/ridge/solvers/cuml.py
+++ b/benchmarks/ridge/solvers/cuml.py
@@ -63,7 +63,9 @@ def skip(self, **objective_dict):
             return True, "Multitarget is not supported."
 
         solver = objective_dict["solver"]
-        if solver not in ["svd", "cd", "eig"]:
+        # NB: should also support "cd" but it doesnt work
+        # TODO: investigate ?
+        if solver not in ["svd", "eig"]:
             return True, "Only accepts the svd solver at the moment."
 
         return False, None
@@ -73,7 +75,7 @@ def warm_up(self):
         n_warmup_features = 5
         sample_weight = self.sample_weight
         if sample_weight is not None:
-            sample_weight = (sample_weight[:n_warmup_samples].copy(),)
+            sample_weight = sample_weight[:n_warmup_samples].copy()
         cuml.Ridge(
             alpha=self.alpha,
             fit_intercept=self.fit_intercept,

From d2ccf5ba5141be15f0392f8e85094699b774970e Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Thu, 18 Jan 2024 13:25:39 +0100
Subject: [PATCH 40/43] fixup torch

---
 benchmarks/ridge/solvers/sklearn_torch_dispatch.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/benchmarks/ridge/solvers/sklearn_torch_dispatch.py b/benchmarks/ridge/solvers/sklearn_torch_dispatch.py
index f51f829..1ba6213 100644
--- a/benchmarks/ridge/solvers/sklearn_torch_dispatch.py
+++ b/benchmarks/ridge/solvers/sklearn_torch_dispatch.py
@@ -87,7 +87,7 @@ def warm_up(self):
         n_warmup_features = 5
         sample_weight = self.sample_weight
         if sample_weight is not None:
-            sample_weight = sample_weight[:n_warmup_samples].copy()
+            sample_weight = sample_weight[:n_warmup_samples].clone()
         with config_context(array_api_dispatch=True):
             Ridge(
                 alpha=self.alpha,
@@ -99,8 +99,8 @@ def warm_up(self):
                 positive=True if (self.solver == "lbfgs") else False,
                 random_state=self.random_state,
             ).fit(
-                self.X[:n_warmup_samples, :n_warmup_features].copy(),
-                self.y[:n_warmup_samples].copy(),
+                self.X[:n_warmup_samples, :n_warmup_features].clone(),
+                self.y[:n_warmup_samples].clone(),
                 sample_weight,
             )
 

From 5a0da41b3349fcc9c2b3b1a72498c3a7f5d334b8 Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Thu, 18 Jan 2024 14:46:38 +0100
Subject: [PATCH 41/43] Remove lbfgs (because it enforces positive=True)

---
 benchmarks/ridge/objective.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/benchmarks/ridge/objective.py b/benchmarks/ridge/objective.py
index 88c939e..f5520fc 100644
--- a/benchmarks/ridge/objective.py
+++ b/benchmarks/ridge/objective.py
@@ -28,7 +28,6 @@ class Objective(BaseObjective):
             ("sparse_cg", None, 1e-4),
             ("sag", None, 1e-4),
             ("saga", None, 1e-4),
-            ("lbfgs", None, 1e-4),
             ("cd", None, 1e-4),
             ("eig", None, None),
             # Used for scikit-learn-intelex that doesn't

From 7b4c52e44821a3eb745da628d5f6107af5c32317 Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Thu, 18 Jan 2024 16:29:56 +0100
Subject: [PATCH 42/43] Update all results.

---
 .../benchopt_run_2024-01-17_12h14m29.parquet  | Bin 36368 -> 0 bytes
 .../benchopt_run_2024-01-17_13h01m18.parquet  | Bin 34214 -> 0 bytes
 .../benchopt_run_2024-01-18_11h26m04.parquet  | Bin 0 -> 34305 bytes
 .../benchopt_run_2024-01-18_12h59m51.parquet  | Bin 0 -> 38350 bytes
 .../benchopt_run_2024-01-18_13h35m10.parquet  | Bin 0 -> 34455 bytes
 .../benchopt_run_2024-01-18_16h06m03.parquet  | Bin 0 -> 34395 bytes
 benchmarks/ridge/results.csv                  | 228 ++++++++++++------
 7 files changed, 156 insertions(+), 72 deletions(-)
 delete mode 100644 benchmarks/ridge/outputs/benchopt_run_2024-01-17_12h14m29.parquet
 delete mode 100644 benchmarks/ridge/outputs/benchopt_run_2024-01-17_13h01m18.parquet
 create mode 100644 benchmarks/ridge/outputs/benchopt_run_2024-01-18_11h26m04.parquet
 create mode 100644 benchmarks/ridge/outputs/benchopt_run_2024-01-18_12h59m51.parquet
 create mode 100644 benchmarks/ridge/outputs/benchopt_run_2024-01-18_13h35m10.parquet
 create mode 100644 benchmarks/ridge/outputs/benchopt_run_2024-01-18_16h06m03.parquet

diff --git a/benchmarks/ridge/outputs/benchopt_run_2024-01-17_12h14m29.parquet b/benchmarks/ridge/outputs/benchopt_run_2024-01-17_12h14m29.parquet
deleted file mode 100644
index 6b5e12972a9f38149bdac7303c27cf583659754a..0000000000000000000000000000000000000000
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diff --git a/benchmarks/ridge/results.csv b/benchmarks/ridge/results.csv
index 270518c..8ad111b 100644
--- a/benchmarks/ridge/results.csv
+++ b/benchmarks/ridge/results.csv
@@ -1,73 +1,157 @@
 Benchmark id,Dtype,Nb data samples,Nb data features,Nb data targets,Regularization strength,Walltime,Backend provider,Compute device,Compute runtime,Solver,Nb cpus,Cpu name,Gpu name,RAM (GB),Platform,Platform architecture,Platform release,Run date,Version info,Comment,Result nb iterations,Result objective value,Data random state,Solver random state
-cbe96b03,float32,10000000,100,1,1.0,0.563,scikit-learn-intelex,cpu,,DefaultDense,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn-intelex 2024.0.1,,,3.105119922031021,123,123
-cbe96b03,float32,10000000,100,1,1.0,0.79,sklearn-torch-dispatch,cuda,,svd,32,AMD EPYC 7402 24-Core Processor,NVIDIA A100-PCIE-40GB,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,3.10511992203598,123,123
-cbe96b03,float32,10000000,100,1,1.0,3.42,scikit-learn,,,cholesky,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,,3.1051197220314024,123,123
-cbe96b03,float32,10000000,100,1,1.0,5.96,scikit-learn,,,lsqr,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,17,3.105119122049713,123,123
-cbe96b03,float32,10000000,100,1,1.0,8.91,sklearn-torch-dispatch,cpu,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,3.1051209226127625,123,123
-cbe96b03,float32,10000000,100,1,1.0,52.7,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,,3.105120122029496,123,123
-f976a4f8,float32,10000000,100,1,1e-10,0.408,scikit-learn-intelex,cpu,,DefaultDense,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn-intelex 2024.0.1,,,3.1051164000000004,123,123
-f976a4f8,float32,10000000,100,1,1e-10,0.742,sklearn-torch-dispatch,cuda,,svd,32,AMD EPYC 7402 24-Core Processor,NVIDIA A100-PCIE-40GB,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,3.1051164000000004,123,123
-f976a4f8,float32,10000000,100,1,1e-10,3.45,scikit-learn,,,cholesky,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,,3.1051164000000004,123,123
-f976a4f8,float32,10000000,100,1,1e-10,6.14,scikit-learn,,,lsqr,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,17,3.1051158000000005,123,123
-f976a4f8,float32,10000000,100,1,1e-10,9.2,sklearn-torch-dispatch,cpu,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,3.1051176000000003,123,123
-f976a4f8,float32,10000000,100,1,1e-10,52.3,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,,3.1051168,123,123
-a9625843,float32,10000000,100,10,1.0,0.452,scikit-learn-intelex,cpu,,DefaultDense,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn-intelex 2024.0.1,,,2.692140948616333,123,123
-a9625843,float32,10000000,100,10,1.0,0.75,sklearn-torch-dispatch,cuda,,svd,32,AMD EPYC 7402 24-Core Processor,NVIDIA A100-PCIE-40GB,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,2.692140628620148,123,123
-a9625843,float32,10000000,100,10,1.0,3.94,scikit-learn,,,cholesky,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,,2.692140308622131,123,123
-a9625843,float32,10000000,100,10,1.0,9.93,sklearn-torch-dispatch,cpu,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,2.6921425489894104,123,123
-a9625843,float32,10000000,100,10,1.0,48.9,scikit-learn,,,lsqr,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,18,2.6921428686135864,123,123
-a9625843,float32,10000000,100,10,1.0,52.8,scikit-learn,,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,,2.69214062861969,123,123
-8ed75954,float32,10000000,100,10,1e-10,0.44,scikit-learn-intelex,cpu,,DefaultDense,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn-intelex 2024.0.1,,,2.69213856,123,123
-8ed75954,float32,10000000,100,10,1e-10,0.749,sklearn-torch-dispatch,cuda,,svd,32,AMD EPYC 7402 24-Core Processor,NVIDIA A100-PCIE-40GB,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,2.69213824,123,123
-8ed75954,float32,10000000,100,10,1e-10,3.89,scikit-learn,,,cholesky,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,,2.69213792,123,123
-8ed75954,float32,10000000,100,10,1e-10,9.78,sklearn-torch-dispatch,cpu,,svd,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0; torch 2.1.0.post300,,,2.69214016,123,123
-8ed75954,float32,10000000,100,10,1e-10,48.6,scikit-learn,,,lsqr,32,AMD EPYC 7302 16-Core Processor,,503,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-17,scikit-learn 1.5.dev0,,18,2.69214048,123,123
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+2f926629,float32,5000,5000,10,1e-10,5.85,sklearn-torch-dispatch,cpu,,svd,32,AMD EPYC 7402 24-Core Processor,,1008,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-18,scikit-learn 1.5.dev0; torch 2.1.0.post100,,,1.004838915901758e-05,123,123
+2f926629,float32,5000,5000,10,1e-10,9.98,scikit-learn,,,svd,32,Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz,,377,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-18,scikit-learn 1.3.2,,,7.194650675920532e-06,123,123
+2f926629,float32,5000,5000,10,1e-10,10.5,scikit-learn,,,cholesky,32,Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz,,377,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-18,scikit-learn 1.3.2,,,7.194650675920532e-06,123,123
+2f926629,float32,5000,5000,10,1e-10,19.9,scikit-learn,,,lsqr,32,Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz,,377,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-18,scikit-learn 1.3.2,,652,0.4086182426040169,123,123
+2f926629,float32,5000,5000,10,1e-10,93.1,scikit-learn,,,sparse_cg,32,Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz,,377,Linux,x86_64,4.18.0-394.el8.x86_64,2024-01-18,scikit-learn 1.3.2,,,0.0719987724515187,123,123

From 92aa8331319daba56b29cd3cf59bb5c0caecd3bc Mon Sep 17 00:00:00 2001
From: Franck Charras <29153872+fcharras@users.noreply.github.com>
Date: Wed, 21 Feb 2024 12:15:22 +0100
Subject: [PATCH 43/43] Fix a bug that didn't reset bold settings after a sync
 refresh

---
 benchmarks/kmeans/consolidate_result_csv.py | 7 ++++---
 benchmarks/pca/consolidate_result_csv.py    | 7 ++++---
 benchmarks/ridge/consolidate_result_csv.py  | 7 ++++---
 3 files changed, 12 insertions(+), 9 deletions(-)

diff --git a/benchmarks/kmeans/consolidate_result_csv.py b/benchmarks/kmeans/consolidate_result_csv.py
index 81e9a50..5656264 100644
--- a/benchmarks/kmeans/consolidate_result_csv.py
+++ b/benchmarks/kmeans/consolidate_result_csv.py
@@ -392,10 +392,11 @@ def _gspread_sync(source, gspread_url, gspread_auth_key):
         worksheet.freeze(0, 0)
         worksheet.resize(rows=n_rows + 1, cols=n_cols)
         worksheet.clear_notes(global_range)
-        white_background = dict(
-            backgroundColorStyle=dict(rgbColor=dict(red=1, green=1, blue=1, alpha=1))
+        reset_format = dict(
+            backgroundColorStyle=dict(rgbColor=dict(red=1, green=1, blue=1, alpha=1)),
+            textFormat=dict(bold=False),
         )
-        worksheet.format(global_range, white_background)
+        worksheet.format(global_range, reset_format)
     except gspread.WorksheetNotFound:
         worksheet = sheet.add_worksheet(
             GOOGLE_WORKSHEET_NAME, rows=n_rows + 1, cols=n_cols
diff --git a/benchmarks/pca/consolidate_result_csv.py b/benchmarks/pca/consolidate_result_csv.py
index fc34b3f..90835e3 100644
--- a/benchmarks/pca/consolidate_result_csv.py
+++ b/benchmarks/pca/consolidate_result_csv.py
@@ -390,10 +390,11 @@ def _gspread_sync(source, gspread_url, gspread_auth_key):
         worksheet.freeze(0, 0)
         worksheet.resize(rows=n_rows + 1, cols=n_cols)
         worksheet.clear_notes(global_range)
-        white_background = dict(
-            backgroundColorStyle=dict(rgbColor=dict(red=1, green=1, blue=1, alpha=1))
+        reset_format = dict(
+            backgroundColorStyle=dict(rgbColor=dict(red=1, green=1, blue=1, alpha=1)),
+            textFormat=dict(bold=False),
         )
-        worksheet.format(global_range, white_background)
+        worksheet.format(global_range, reset_format)
     except gspread.WorksheetNotFound:
         worksheet = sheet.add_worksheet(
             GOOGLE_WORKSHEET_NAME, rows=n_rows + 1, cols=n_cols
diff --git a/benchmarks/ridge/consolidate_result_csv.py b/benchmarks/ridge/consolidate_result_csv.py
index dc15978..892cbfd 100644
--- a/benchmarks/ridge/consolidate_result_csv.py
+++ b/benchmarks/ridge/consolidate_result_csv.py
@@ -390,10 +390,11 @@ def _gspread_sync(source, gspread_url, gspread_auth_key):
         worksheet.freeze(0, 0)
         worksheet.resize(rows=n_rows + 1, cols=n_cols)
         worksheet.clear_notes(global_range)
-        white_background = dict(
-            backgroundColorStyle=dict(rgbColor=dict(red=1, green=1, blue=1, alpha=1))
+        reset_format = dict(
+            backgroundColorStyle=dict(rgbColor=dict(red=1, green=1, blue=1, alpha=1)),
+            textFormat=dict(bold=False),
         )
-        worksheet.format(global_range, white_background)
+        worksheet.format(global_range, reset_format)
     except gspread.WorksheetNotFound:
         worksheet = sheet.add_worksheet(
             GOOGLE_WORKSHEET_NAME, rows=n_rows + 1, cols=n_cols