index int64 0 0 | repo_id stringlengths 16 145 | file_path stringlengths 27 196 | content stringlengths 1 16.7M |
|---|---|---|---|
0 | capitalone_repos | capitalone_repos/rubicon-ml/.pre-commit-config.yaml | repos:
- repo: https://github.com/psf/black
rev: 23.7.0
hooks:
- id: black
exclude: (versioneer.py|_version.py)
- repo: https://github.com/timothycrosley/isort
rev: 5.12.0
hooks:
- id: isort
- repo: https://github.com/pycqa/flake8
rev: 6.1.0
hooks:
- id: flake8
|
0 | capitalone_repos | capitalone_repos/rubicon-ml/setup.cfg | [metadata]
name = rubicon-ml
description = "an ML library for model development and governance"
long_description = file: README.md
long_description_content_type = text/markdown
author = "Joe Wolfe, Ryan Soley, Diane Lee, Mike McCarty, CapitalOne"
license = "Apache License, Version 2.0"
url = https://github.com/capitalo... |
0 | capitalone_repos | capitalone_repos/rubicon-ml/.coveragerc | [report]
exclude_lines =
pragma: no cover
raise AssertionError
raise NotImplementedError
if __name__ == .__main__.:
omit =
versioneer.py
rubicon_ml/_version.py
|
0 | capitalone_repos | capitalone_repos/rubicon-ml/versioneer.py |
# Version: 0.19
"""The Versioneer - like a rocketeer, but for versions.
The Versioneer
==============
* like a rocketeer, but for versions!
* https://github.com/python-versioneer/python-versioneer
* Brian Warner
* License: Public Domain
* Compatible with: Python 3.6, 3.7, 3.8, 3.9 and pypy3
* [![Latest Version][pyp... |
0 | capitalone_repos | capitalone_repos/rubicon-ml/conftest.py | pytest_plugins = [
"tests.fixtures",
]
def pytest_addoption(parser):
parser.addoption("--s3-path", dest="s3-path")
|
0 | capitalone_repos | capitalone_repos/rubicon-ml/LICENSE | Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
... |
0 | capitalone_repos | capitalone_repos/rubicon-ml/pyproject.toml | [tool.black]
line-length = 100
exclude = "(versioneer.py|_version.py)"
|
0 | capitalone_repos | capitalone_repos/rubicon-ml/CODEOWNERS | * @capitalone/rubicon-admin-team
|
0 | capitalone_repos | capitalone_repos/rubicon-ml/MANIFEST.in | graft rubicon_ml/viz/assets
graft rubicon_ml/viz/assets/css
include versioneer.py
include rubicon_ml/_version.py
recursive-include rubicon_ml/schema *.yaml
|
0 | capitalone_repos | capitalone_repos/rubicon-ml/README.md | # rubicon-ml
[](https://github.com/capitalone/rubicon-ml/actions/workflows/test-package.yml)
[](https://githu... |
0 | capitalone_repos | capitalone_repos/rubicon-ml/environment.yml | name: rubicon-ml-dev
channels:
- conda-forge
dependencies:
- python>=3.8
- pip
- click<=8.1.7,>=7.1
- fsspec<=2023.9.2,>=2021.4.0
- intake[dataframe]<=0.7.0,>=0.5.2
- jsonpath-ng<=1.6.0,>=1.5.3
- numpy<=1.26.0,>=1.22.0
- pandas<=2.1.1,>=1.0.0
- pyarrow<=13.0.0,>=0.18.0
- PyYAML<=6.0.1,>=5.4.0
-... |
0 | capitalone_repos | capitalone_repos/rubicon-ml/setup.py | """Setup file for the package. For configuration information, see the ``setup.cfg``."""
from setuptools import setup
import versioneer
setup(
version=versioneer.get_version(),
cmdclass=versioneer.get_cmdclass(),
)
|
0 | capitalone_repos/rubicon-ml | capitalone_repos/rubicon-ml/notebooks/user-environment.yml | name: rubicon-ml
channels:
- conda-forge
dependencies:
- python>=3.8
- pip
- jupyterlab
- rubicon-ml
|
0 | capitalone_repos/rubicon-ml | capitalone_repos/rubicon-ml/notebooks/README.md | # rubicon-ml notebooks
These notebooks are interactive versions of the examples found in our
documentation. You can clone the repo and run the examples on your own, or just
take a look at their outputs here!
If you're a rubicon-ml user that wants to run the examples, check out the first
section, **Users** to get set ... |
0 | capitalone_repos/rubicon-ml/notebooks | capitalone_repos/rubicon-ml/notebooks/logging-examples/tagging.ipynb | from rubicon_ml import Rubicon
import pandas as pd
rubicon = Rubicon(persistence="memory")
project = rubicon.get_or_create_project("Tagging")
#logging experiments with tags
experiment1 = project.log_experiment(name="experiment1", tags=["odd_num_exp"])
experiment2 = project.log_experiment(name="experiment2", tags=["ev... |
0 | capitalone_repos/rubicon-ml/notebooks | capitalone_repos/rubicon-ml/notebooks/logging-examples/logging-plots.ipynb | import os
from rubicon_ml import Rubicon
rubicon = Rubicon(persistence="memory")
project = rubicon.get_or_create_project("Artifact Plots")import plotly.express as px
from plotly import data
df = data.wind()
df.head()scatter_plot = px.scatter(df, x="direction", y="frequency", color="strength")
scatter_plot.write_im... |
0 | capitalone_repos/rubicon-ml/notebooks | capitalone_repos/rubicon-ml/notebooks/logging-examples/set-schema.ipynb | from rubicon_ml.schema import registry
available_schema = registry.available_schema()
available_schemaimport pprint
rfc_schema = registry.get_schema("sklearn__RandomForestClassifier")
pprint.pprint(rfc_schema)from rubicon_ml import Rubicon
rubicon = Rubicon(persistence="memory")
project = rubicon.create_project(name... |
0 | capitalone_repos/rubicon-ml/notebooks | capitalone_repos/rubicon-ml/notebooks/logging-examples/multiple-backend.ipynb | from rubicon_ml import Rubicon#rb = Rubicon(persistence="memory")
#or
#rb = Rubicon(persistence="filesystem")#example multiple backend instantiaiton
rb = Rubicon(composite_config=[
{"persistence": "filesystem", "root_dir": "./rubicon-root/rootA"},
{"persistence": "filesystem", "root_dir": "./rubicon-root/rootB"... |
0 | capitalone_repos/rubicon-ml/notebooks | capitalone_repos/rubicon-ml/notebooks/logging-examples/logging_concurrently.py | from collections import namedtuple
SklearnTrainingMetadata = namedtuple("SklearnTrainingMetadata", "module_name method")
def run_experiment(project, classifier_cls, wine_datasets, feature_names, **kwargs):
X_train, X_test, y_train, y_test = wine_datasets
experiment = project.log_experiment(
training... |
0 | capitalone_repos/rubicon-ml/notebooks | capitalone_repos/rubicon-ml/notebooks/logging-examples/visualizing-logged-dataframes.ipynb | import os
from rubicon_ml import Rubicon
rubicon = Rubicon(persistence="memory")
project = rubicon.get_or_create_project("Plotting Example")
projectimport pandas as pd
df = pd.DataFrame.from_records(
[
["Walmart", 514405],
["Exxon Mobil", 290212],
["Apple", 265595],
["Berkshire... |
0 | capitalone_repos/rubicon-ml/notebooks | capitalone_repos/rubicon-ml/notebooks/logging-examples/logging-concurrently.ipynb | import os
from rubicon_ml import Rubicon
root_dir = os.environ.get("RUBICON_ROOT", "rubicon-root")
root_path = f"{os.path.dirname(os.getcwd())}/{root_dir}"
rubicon = Rubicon(persistence="filesystem", root_dir=root_path)
project = rubicon.get_or_create_project(
"Concurrent Experiments",
description="training... |
0 | capitalone_repos/rubicon-ml/notebooks | capitalone_repos/rubicon-ml/notebooks/logging-examples/logging-training-metadata.ipynb | s3_config = {
"region_name": "us-west-2",
"signature_version": "v4",
"retries": {
"max_attempts": 10,
"mode": "standard",
}
}
bucket_name = "my-bucket"
key = "path/to/my/data.parquet"def read_from_s3(config, bucket, key, local_output_path):
import boto3
from botocore.config impo... |
0 | capitalone_repos/rubicon-ml/notebooks | capitalone_repos/rubicon-ml/notebooks/logging-examples/rubiconJSON-querying.ipynb | from rubicon_ml import Rubicon
from sklearn.datasets import load_wine
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, make_scorer, precision_score, recall_score
from sklearn.model_selection import ParameterGrid, train_test_splitX, y = load_wine(return_X_y=True, as_frame=... |
0 | capitalone_repos/rubicon-ml/notebooks | capitalone_repos/rubicon-ml/notebooks/logging-examples/logging-feature-plots.ipynb | import shap
import sklearn
from sklearn.datasets import load_wine
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.preprocessing import StandardScaler
from rubicon_ml import Rubicon
from rubicon_ml.sklearn import make_pipeline
rubicon = Rubicon(persistence="memory")
project = rubicon.get_or_create... |
0 | capitalone_repos/rubicon-ml/notebooks | capitalone_repos/rubicon-ml/notebooks/logging-examples/register-custom-schema.ipynb | import os
os.environ["RUNTIME_ENV"] = "AWS"
! echo $RUNTIME_ENVimport pprint
extended_schema = {
"name": "sklearn__RandomForestClassifier__ext",
"extends": "sklearn__RandomForestClassifier",
"parameters": [
{"name": "runtime_environment", "value_env": "RUNTIME_ENV"},
],
}
pprint.pprint(e... |
0 | capitalone_repos/rubicon-ml/notebooks | capitalone_repos/rubicon-ml/notebooks/logging-examples/log-with-schema.ipynb | from rubicon_ml import Rubicon
rubicon = Rubicon(persistence="memory", auto_git_enabled=True)
project = rubicon.create_project(name="apply schema")
projectfrom sklearn.datasets import load_wine
X, y = load_wine(return_X_y=True, as_frame=True)from sklearn.ensemble import RandomForestClassifier
rfc = RandomForestClass... |
0 | capitalone_repos/rubicon-ml/notebooks | capitalone_repos/rubicon-ml/notebooks/logging-examples/logging-experiment-failures.ipynb | from sklearn.base import BaseEstimator
import random
class BadEstimator(BaseEstimator):
def __init__(self):
super().__init__()
self.knn = KNeighborsClassifier(n_neighbors=2)
def fit(self, X, y):
self.knn.fit(X, y)
output=random.random()
if output>.3:
self.sta... |
0 | capitalone_repos/rubicon-ml/notebooks | capitalone_repos/rubicon-ml/notebooks/quick-look/visualizing-experiments.ipynb | import intake
import rubicon_ml
catalog = intake.open_catalog("./penguin_catalog.yml")
for source in catalog:
catalog[source].discover()
experiments = [catalog[source].read() for source in catalog]from rubicon_ml.viz import ExperimentsTable
ExperimentsTable(experiments=experiments).show()from rubicon_ml.viz... |
0 | capitalone_repos/rubicon-ml/notebooks | capitalone_repos/rubicon-ml/notebooks/quick-look/logging-experiments.ipynb | from palmerpenguins import load_penguins
penguins_df = load_penguins()
target_values = penguins_df['species'].unique()
print(f"target classes (species): {target_values}")
penguins_df.head()from sklearn.preprocessing import LabelEncoder
for column in ["species", "island", "sex"]:
penguins_df[column] = LabelEncode... |
0 | capitalone_repos/rubicon-ml/notebooks | capitalone_repos/rubicon-ml/notebooks/quick-look/sharing-experiments.ipynb | from rubicon_ml import Rubicon
rubicon = Rubicon(persistence="filesystem", root_dir="./rubicon-root")
project = rubicon.get_project(name="classifying penguins")
projectfrom rubicon_ml import publish
catalog = publish(
project.experiments(tags=["parameter search"]),
output_filepath="./penguin_catalog.yml",
)
... |
0 | capitalone_repos/rubicon-ml/notebooks | capitalone_repos/rubicon-ml/notebooks/demos/classification.ipynb | from palmerpenguins import load_penguins
penguins_df = load_penguins()
penguins_df.head()import plotly.express as px
px.scatter(penguins_df, x="flipper_length_mm", y="bill_length_mm", color="species")from sklearn.preprocessing import LabelEncoder
penguin_encoder = LabelEncoder()
for column in ["species", "island", ... |
0 | capitalone_repos/rubicon-ml/notebooks | capitalone_repos/rubicon-ml/notebooks/integrations/integration-prefect-workflows.ipynb | from rubicon_ml.workflow.prefect import (
get_or_create_project_task,
create_experiment_task,
log_artifact_task,
log_dataframe_task,
log_feature_task,
log_metric_task,
log_parameter_task,
)from prefect import task
@task
def load_data():
from sklearn.datasets import load_wine
r... |
0 | capitalone_repos/rubicon-ml/notebooks | capitalone_repos/rubicon-ml/notebooks/integrations/integration-git.ipynb | from rubicon_ml import Rubicon
rubicon = Rubicon(persistence="memory", auto_git_enabled=True)project = rubicon.create_project("Automatic Git Integration")
project.github_urlexperiment = project.log_experiment(model_name="GitHub Model")
experiment.branch_name, experiment.commit_hashfrom rubicon_ml.viz import Dashboa... |
0 | capitalone_repos/rubicon-ml/notebooks | capitalone_repos/rubicon-ml/notebooks/integrations/integration-sklearn.ipynb | from sklearn.svm import SVC
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from rubicon_ml import Rubicon
from rubicon_ml.sklearn import RubiconPipeline
rubicon = Rubicon(pers... |
0 | capitalone_repos/rubicon-ml/notebooks | capitalone_repos/rubicon-ml/notebooks/viz/dataframe-plot.ipynb | import random
import numpy as np
import pandas as pd
import plotly.express as px
from rubicon_ml import Rubicon
from rubicon_ml.viz import DataframePlotDISPLAY_DFS = False
rubicon = Rubicon(persistence="memory", auto_git_enabled=True)
project = rubicon.get_or_create_project("plot comparison")
num_experiments_to_log... |
0 | capitalone_repos/rubicon-ml/notebooks | capitalone_repos/rubicon-ml/notebooks/viz/metric-correlation-plot.ipynb | import random
from rubicon_ml import Rubicon
from rubicon_ml.viz import MetricCorrelationPlotrubicon = Rubicon(persistence="memory", auto_git_enabled=True)
project = rubicon.get_or_create_project("metric correlation plot")
for i in range(0, 100):
experiment = project.log_experiment()
experiment.log_parameter... |
0 | capitalone_repos/rubicon-ml/notebooks | capitalone_repos/rubicon-ml/notebooks/viz/experiments-table.ipynb | import random
from rubicon_ml import Rubicon
from rubicon_ml.viz import ExperimentsTablerubicon = Rubicon(persistence="memory", auto_git_enabled=True)
project = rubicon.get_or_create_project("experiment table")
for i in range(0, 24):
experiment = project.log_experiment()
experiment.log_parameter(name="max_dep... |
0 | capitalone_repos/rubicon-ml/notebooks | capitalone_repos/rubicon-ml/notebooks/viz/dashboard.ipynb | import random
import numpy as np
import pandas as pd
from rubicon_ml import Rubicon
from rubicon_ml.viz import (
DataframePlot,
ExperimentsTable,
MetricCorrelationPlot,
MetricListsComparison,
)
from rubicon_ml.viz.dashboard import Dashboarddates = pd.date_range(start="1/1/2010", end="12/1/2020", freq=... |
0 | capitalone_repos/rubicon-ml/notebooks | capitalone_repos/rubicon-ml/notebooks/viz/metric-lists-comparisons.ipynb | import random
from rubicon_ml import Rubicon
from rubicon_ml.viz import MetricListsComparisonrubicon = Rubicon(persistence="memory", auto_git_enabled=True)
project = rubicon.get_or_create_project("list metric comparison")
for i in range(0, 10):
experiment = project.log_experiment()
experiment.log_metric(
... |
0 | capitalone_repos/rubicon-ml/notebooks | capitalone_repos/rubicon-ml/notebooks/tutorials/failure-modes.ipynb | from rubicon_ml import Rubicon
rb = Rubicon(persistence="memory")
rb.get_project(name="failure modes")from rubicon_ml import set_failure_mode
set_failure_mode("warn")
rb.get_project(name="failure modes")set_failure_mode("log")
rb.get_project(name="failure modes")set_failure_mode("log", traceback_limit=0)
rb.get_... |
0 | capitalone_repos/rubicon-ml | capitalone_repos/rubicon-ml/tests/fixtures.py | import os
import random
import uuid
import dask.array as da
import dask.dataframe as dd
import numpy as np
import pandas as pd
import pytest
from dask.distributed import Client
from sklearn.datasets import make_classification
from rubicon_ml import Rubicon
from rubicon_ml.repository import MemoryRepository
class _A... |
0 | capitalone_repos/rubicon-ml/tests | capitalone_repos/rubicon-ml/tests/notebooks/test_notebooks.py | import os
from unittest import mock
import fsspec
import pytest
from nbconvert.preprocessors import ExecutePreprocessor
from tests.notebooks.utils import get_notebook_filenames, read_notebook_file
NOTEBOOK_FILENAMES = get_notebook_filenames(
os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))... |
0 | capitalone_repos/rubicon-ml/tests | capitalone_repos/rubicon-ml/tests/notebooks/utils.py | import json
import os
import fsspec
import nbformat
DEFAULT_NBFORMAT_VERSION = 4
def get_notebook_filenames(root_path):
fs = fsspec.filesystem("file")
notebook_glob = os.path.join(root_path, "*.ipynb")
nested_notebook_glob = os.path.join(root_path, "**", "*.ipynb")
notebook_filenames = fs.glob(not... |
0 | capitalone_repos/rubicon-ml/tests/notebooks | capitalone_repos/rubicon-ml/tests/notebooks/bad-notebooks/not-executed.ipynb | x = 1y = 2z = 3 |
0 | capitalone_repos/rubicon-ml/tests/notebooks | capitalone_repos/rubicon-ml/tests/notebooks/bad-notebooks/empty-last-cell.ipynb | x = 1y = 2z = 3 |
0 | capitalone_repos/rubicon-ml/tests/notebooks | capitalone_repos/rubicon-ml/tests/notebooks/bad-notebooks/not-executed-in-order.ipynb | x = 1y = 2z = 3 |
0 | capitalone_repos/rubicon-ml/tests/notebooks | capitalone_repos/rubicon-ml/tests/notebooks/bad-notebooks/not-all-executed.ipynb | x = 1y = 2z = 3 |
0 | capitalone_repos/rubicon-ml/tests | capitalone_repos/rubicon-ml/tests/integration/test_misc_dotfiles.py | import os
import warnings
def test_rubicon_with_misc_folders_at_project_level(rubicon_local_filesystem_client_with_project):
rubicon, project = rubicon_local_filesystem_client_with_project
os.makedirs(os.path.join(rubicon.config.root_dir, ".ipynb_checkpoints"))
with warnings.catch_warnings(record=True) ... |
0 | capitalone_repos/rubicon-ml/tests | capitalone_repos/rubicon-ml/tests/integration/test_dataframe_logging.py | import pandas as pd
import pytest
from dask import dataframe as dd
from rubicon_ml.exceptions import RubiconException
def test_pandas_df(rubicon_local_filesystem_client):
rubicon = rubicon_local_filesystem_client
project = rubicon.create_project("Dataframe Test Project")
multi_index_df = pd.DataFrame(
... |
0 | capitalone_repos/rubicon-ml/tests | capitalone_repos/rubicon-ml/tests/integration/test_prefect_flow.py | import numpy as np
import pandas as pd
from prefect import Flow
from rubicon_ml import Rubicon
from rubicon_ml.client import (
Artifact,
Dataframe,
Experiment,
Feature,
Metric,
Parameter,
Project,
)
from rubicon_ml.workflow.prefect import (
create_experiment_task,
get_or_create_proj... |
0 | capitalone_repos/rubicon-ml/tests | capitalone_repos/rubicon-ml/tests/integration/test_schema.py | import pytest
from lightgbm import LGBMClassifier, LGBMRegressor
from sklearn.ensemble import RandomForestClassifier
from xgboost import XGBClassifier, XGBRegressor
from xgboost.dask import DaskXGBClassifier, DaskXGBRegressor
PANDAS_SCHEMA_CLS = [
LGBMClassifier,
LGBMRegressor,
RandomForestClassifier,
... |
0 | capitalone_repos/rubicon-ml/tests | capitalone_repos/rubicon-ml/tests/integration/test_concurrency.py | import multiprocessing
import pandas as pd
from dask import dataframe as dd
from rubicon_ml.domain.utils import uuid
def _log_all_to_experiment(experiment):
ddf = dd.from_pandas(pd.DataFrame([0, 1], columns=["a"]), npartitions=1)
for _ in range(0, 4):
experiment.log_metric(uuid.uuid4(), 0)
... |
0 | capitalone_repos/rubicon-ml/tests | capitalone_repos/rubicon-ml/tests/integration/test_rubicon.py | import uuid
import pandas as pd
import pytest
from rubicon_ml import Rubicon
filesystems = [
pytest.param(Rubicon(persistence="memory")),
pytest.param(
Rubicon(persistence="filesystem", root_dir="./test-rubicon"),
marks=pytest.mark.write_files,
),
pytest.param(
Rubicon(persist... |
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