id int64 1 6.07M | name stringlengths 1 295 | code stringlengths 12 426k | language stringclasses 1
value | source_file stringlengths 5 202 | start_line int64 1 158k | end_line int64 1 158k | repo dict |
|---|---|---|---|---|---|---|---|
2,601 | config | def config(self) -> Dict[str, Any]:
if self._config is not None:
return deepcopy(self._config)
self._config = {k: v["config"] for (k, v) in self._entries.items()}
return deepcopy(self._config) | python | wandb/testing/relay.py | 180 | 185 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,602 | get_run_telemetry | def get_run_telemetry(self, run_id: str) -> Dict[str, Any]:
return self.config.get(run_id, {}).get("_wandb", {}).get("value", {}).get("t") | python | wandb/testing/relay.py | 201 | 202 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,603 | get_run_metrics | def get_run_metrics(self, run_id: str) -> Dict[str, Any]:
return self.config.get(run_id, {}).get("_wandb", {}).get("value", {}).get("m") | python | wandb/testing/relay.py | 204 | 205 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,604 | get_run_summary | def get_run_summary(
self, run_id: str, include_private: bool = False
) -> Dict[str, Any]:
# run summary dataframe must have only one row
# for the given run id, so we convert it to dict
# and extract the first (and only) row.
mask_run = self.summary["__run_id"] == run_id
run_summary = self.summary[mask_run]
ret = (
run_summary.filter(regex="^[^_]", axis=1)
if not include_private
else run_summary
).to_dict(orient="records")
return ret[0] if len(ret) > 0 else {} | python | wandb/testing/relay.py | 207 | 220 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,605 | get_run_history | def get_run_history(
self, run_id: str, include_private: bool = False
) -> pd.DataFrame:
mask_run = self.history["__run_id"] == run_id
run_history = self.history[mask_run]
return (
run_history.filter(regex="^[^_]", axis=1)
if not include_private
else run_history
) | python | wandb/testing/relay.py | 222 | 231 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,606 | get_run_uploaded_files | def get_run_uploaded_files(self, run_id: str) -> Dict[str, Any]:
return self.entries.get(run_id, {}).get("uploaded", []) | python | wandb/testing/relay.py | 233 | 234 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,607 | get_run_stats | def get_run_stats(self, run_id: str) -> pd.DataFrame:
mask_run = self.events["__run_id"] == run_id
run_stats = self.events[mask_run]
return run_stats | python | wandb/testing/relay.py | 236 | 239 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,608 | __init__ | def __init__(self):
self.resolvers: List["Resolver"] = [
{
"name": "upsert_bucket",
"resolver": self.resolve_upsert_bucket,
},
{
"name": "upload_files",
"resolver": self.resolve_upload_files,
},
{
"name": "uploaded_files",
"resolver": self.resolve_uploaded_files,
},
{
"name": "preempting",
"resolver": self.resolve_preempting,
},
{
"name": "upsert_sweep",
"resolver": self.resolve_upsert_sweep,
},
# { "name": "create_artifact",
# "resolver": self.resolve_create_artifact,
# },
] | python | wandb/testing/relay.py | 250 | 275 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,609 | resolve_upsert_bucket | def resolve_upsert_bucket(
request_data: Dict[str, Any], response_data: Dict[str, Any], **kwargs: Any
) -> Optional[Dict[str, Any]]:
if not isinstance(request_data, dict) or not isinstance(response_data, dict):
return None
query = response_data.get("data", {}).get("upsertBucket") is not None
if query:
data = response_data["data"]["upsertBucket"].get("bucket")
data["config"] = json.loads(data["config"])
return data
return None | python | wandb/testing/relay.py | 278 | 288 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,610 | resolve_upload_files | def resolve_upload_files(
request_data: Dict[str, Any], response_data: Dict[str, Any], **kwargs: Any
) -> Optional[Dict[str, Any]]:
if not isinstance(request_data, dict):
return None
query = request_data.get("files") is not None
if query:
# todo: refactor this 🤮🤮🤮🤮🤮 eventually?
name = kwargs.get("path").split("/")[2]
files = {
file_name: [
{
"content": [
json.loads(k) for k in file_value.get("content", [])
],
"offset": file_value.get("offset"),
}
]
for file_name, file_value in request_data["files"].items()
}
post_processed_data = {
"name": name,
"dropped": [request_data["dropped"]],
"files": files,
}
return post_processed_data
return None | python | wandb/testing/relay.py | 291 | 317 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,611 | resolve_uploaded_files | def resolve_uploaded_files(
request_data: Dict[str, Any], response_data: Dict[str, Any], **kwargs: Any
) -> Optional[Dict[str, Any]]:
if not isinstance(request_data, dict) or not isinstance(response_data, dict):
return None
query = "RunUploadUrls" in request_data.get("query", "")
if query:
# todo: refactor this 🤮🤮🤮🤮🤮 eventually?
name = request_data["variables"]["run"]
files = (
response_data.get("data", {})
.get("model", {})
.get("bucket", {})
.get("files", {})
.get("edges", [])
)
# note: we count all attempts to upload files
post_processed_data = {
"name": name,
"uploaded": [files[0].get("node", {}).get("name")] if files else [""],
}
return post_processed_data
return None | python | wandb/testing/relay.py | 320 | 342 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,612 | resolve_preempting | def resolve_preempting(
request_data: Dict[str, Any], response_data: Dict[str, Any], **kwargs: Any
) -> Optional[Dict[str, Any]]:
if not isinstance(request_data, dict):
return None
query = "preempting" in request_data
if query:
name = kwargs.get("path").split("/")[2]
post_processed_data = {
"name": name,
"preempting": [request_data["preempting"]],
}
return post_processed_data
return None | python | wandb/testing/relay.py | 345 | 358 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,613 | resolve_upsert_sweep | def resolve_upsert_sweep(
request_data: Dict[str, Any], response_data: Dict[str, Any], **kwargs: Any
) -> Optional[Dict[str, Any]]:
if not isinstance(response_data, dict):
return None
query = response_data.get("data", {}).get("upsertSweep") is not None
if query:
data = response_data["data"]["upsertSweep"].get("sweep")
return data
return None | python | wandb/testing/relay.py | 361 | 370 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,614 | resolve_create_artifact | def resolve_create_artifact(
self, request_data: Dict[str, Any], response_data: Dict[str, Any], **kwargs: Any
) -> Optional[Dict[str, Any]]:
if not isinstance(request_data, dict):
return None
query = (
"createArtifact(" in request_data.get("query", "")
and request_data.get("variables") is not None
and response_data is not None
)
if query:
name = request_data["variables"]["runName"]
post_processed_data = {
"name": name,
"create_artifact": [
{
"variables": request_data["variables"],
"response": response_data["data"]["createArtifact"]["artifact"],
}
],
}
return post_processed_data
return None | python | wandb/testing/relay.py | 372 | 394 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,615 | resolve | def resolve(
self,
request_data: Dict[str, Any],
response_data: Dict[str, Any],
**kwargs: Any,
) -> Optional[Dict[str, Any]]:
for resolver in self.resolvers:
result = resolver.get("resolver")(request_data, response_data, **kwargs)
if result is not None:
return result
return None | python | wandb/testing/relay.py | 396 | 406 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,616 | __init__ | def __init__(self, pattern: str):
known_tokens = {self.APPLY_TOKEN, self.PASS_TOKEN, self.STOP_TOKEN}
if not pattern:
raise ValueError("Pattern cannot be empty")
if set(pattern) - known_tokens:
raise ValueError(f"Pattern can only contain {known_tokens}")
self.pattern: "Deque[str]" = deque(pattern) | python | wandb/testing/relay.py | 414 | 421 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,617 | next | def next(self):
if self.pattern[0] == self.STOP_TOKEN:
return
self.pattern.rotate(-1) | python | wandb/testing/relay.py | 423 | 426 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,618 | should_apply | def should_apply(self) -> bool:
return self.pattern[0] == self.APPLY_TOKEN | python | wandb/testing/relay.py | 428 | 429 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,619 | __eq__ | def __eq__(
self,
other: Union["InjectedResponse", requests.Request, requests.PreparedRequest],
):
"""Check InjectedResponse object equality.
We use this to check if this response should be injected as a replacement of
`other`.
:param other:
:return:
"""
if not isinstance(
other, (InjectedResponse, requests.Request, requests.PreparedRequest)
):
return False
# always check the method and url
ret = self.method == other.method and self.url == other.url
# use custom_match_fn to check, e.g. the request body content
if self.custom_match_fn is not None:
ret = ret and self.custom_match_fn(self, other)
return ret | python | wandb/testing/relay.py | 467 | 489 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,620 | to_dict | def to_dict(self):
excluded_fields = {"application_pattern", "custom_match_fn"}
return {
k: self.__getattribute__(k)
for k in self.__dict__
if (not k.startswith("_") and k not in excluded_fields)
} | python | wandb/testing/relay.py | 491 | 497 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,621 | process | def process(self, request: "flask.Request") -> None:
... # pragma: no cover | python | wandb/testing/relay.py | 501 | 502 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,622 | control | def control(self, request: "flask.Request") -> Mapping[str, str]:
... # pragma: no cover | python | wandb/testing/relay.py | 504 | 505 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,623 | __init__ | def __init__(
self,
base_url: str,
inject: Optional[List[InjectedResponse]] = None,
control: Optional[RelayControlProtocol] = None,
) -> None:
# todo for the future:
# - consider switching from Flask to Quart
# - async app will allow for better failure injection/poor network perf
self.relay_control = control
self.app = flask.Flask(__name__)
self.app.logger.setLevel(logging.INFO)
self.app.register_error_handler(DeliberateHTTPError, self.handle_http_exception)
self.app.add_url_rule(
rule="/graphql",
endpoint="graphql",
view_func=self.graphql,
methods=["POST"],
)
self.app.add_url_rule(
rule="/files/<path:path>",
endpoint="files",
view_func=self.file_stream,
methods=["POST"],
)
self.app.add_url_rule(
rule="/storage",
endpoint="storage",
view_func=self.storage,
methods=["PUT", "GET"],
)
self.app.add_url_rule(
rule="/storage/<path:path>",
endpoint="storage_file",
view_func=self.storage_file,
methods=["PUT", "GET"],
)
if control:
self.app.add_url_rule(
rule="/_control",
endpoint="_control",
view_func=self.control,
methods=["POST"],
)
# @app.route("/artifacts/<entity>/<digest>", methods=["GET", "POST"])
self.port = self._get_free_port()
self.base_url = urllib.parse.urlparse(base_url)
self.session = requests.Session()
self.relay_url = f"http://127.0.0.1:{self.port}"
# recursively merge-able object to store state
self.resolver = QueryResolver()
# todo: add an option to add custom resolvers
self.context = Context()
# injected responses
self.inject = inject or []
# useful when debugging:
# self.after_request_fn = self.app.after_request(self.after_request_fn) | python | wandb/testing/relay.py | 509 | 568 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,624 | handle_http_exception | def handle_http_exception(e):
response = e.get_response()
return response | python | wandb/testing/relay.py | 571 | 573 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,625 | _get_free_port | def _get_free_port() -> int:
sock = socket.socket()
sock.bind(("", 0))
_, port = sock.getsockname()
return port | python | wandb/testing/relay.py | 576 | 581 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,626 | start | def start(self) -> None:
# run server in a separate thread
relay_server_thread = threading.Thread(
target=self.app.run,
kwargs={"port": self.port},
daemon=True,
)
relay_server_thread.start() | python | wandb/testing/relay.py | 583 | 590 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,627 | after_request_fn | def after_request_fn(self, response: "requests.Response") -> "requests.Response":
# todo: this is useful for debugging, but should be removed in the future
# flask.request.url = self.relay_url + flask.request.url
print(flask.request)
print(flask.request.get_json())
print(response)
print(response.json())
return response | python | wandb/testing/relay.py | 592 | 599 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,628 | relay | def relay(
self,
request: "flask.Request",
) -> Union["responses.Response", "requests.Response"]:
# replace the relay url with the real backend url (self.base_url)
url = (
urllib.parse.urlparse(request.url)
._replace(netloc=self.base_url.netloc, scheme=self.base_url.scheme)
.geturl()
)
headers = {key: value for (key, value) in request.headers if key != "Host"}
prepared_relayed_request = requests.Request(
method=request.method,
url=url,
headers=headers,
data=request.get_data(),
json=request.get_json(),
).prepare()
for injected_response in self.inject:
# where are we in the application pattern?
should_apply = injected_response.application_pattern.should_apply()
# check if an injected response matches the request
if injected_response == prepared_relayed_request:
# rotate the injection pattern
injected_response.application_pattern.next()
if should_apply:
with responses.RequestsMock() as mocked_responses:
# do the actual injection
mocked_responses.add(**injected_response.to_dict())
relayed_response = self.session.send(prepared_relayed_request)
return relayed_response
# normal case: no injected response matches the request
relayed_response = self.session.send(prepared_relayed_request)
return relayed_response | python | wandb/testing/relay.py | 601 | 637 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,629 | snoop_context | def snoop_context(
self,
request: "flask.Request",
response: "requests.Response",
time_elapsed: float,
**kwargs: Any,
) -> None:
request_data = request.get_json()
response_data = response.json() or {}
if self.relay_control:
self.relay_control.process(request)
# store raw data
raw_data: "RawRequestResponse" = {
"url": request.url,
"request": request_data,
"response": response_data,
"time_elapsed": time_elapsed,
}
self.context.raw_data.append(raw_data)
snooped_context = self.resolver.resolve(request_data, response_data, **kwargs)
if snooped_context is not None:
self.context.upsert(snooped_context)
return None | python | wandb/testing/relay.py | 639 | 665 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,630 | graphql | def graphql(self) -> Mapping[str, str]:
request = flask.request
with Timer() as timer:
relayed_response = self.relay(request)
# print("*****************")
# print("GRAPHQL REQUEST:")
# print(request.get_json())
# print("GRAPHQL RESPONSE:")
# print(relayed_response.status_code, relayed_response.json())
# print("*****************")
# snoop work to extract the context
self.snoop_context(request, relayed_response, timer.elapsed)
# print("*****************")
# print("SNOOPED CONTEXT:")
# print(self.context.entries)
# print(len(self.context.raw_data))
# print("*****************")
return relayed_response.json() | python | wandb/testing/relay.py | 667 | 685 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,631 | file_stream | def file_stream(self, path) -> Mapping[str, str]:
request = flask.request
with Timer() as timer:
relayed_response = self.relay(request)
# print("*****************")
# print("FILE STREAM REQUEST:")
# print("********PATH*********")
# print(path)
# print("********ENDPATH*********")
# print(request.get_json())
# print("FILE STREAM RESPONSE:")
# print(relayed_response)
# print(relayed_response.status_code, relayed_response.json())
# print("*****************")
self.snoop_context(request, relayed_response, timer.elapsed, path=path)
return relayed_response.json() | python | wandb/testing/relay.py | 687 | 704 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,632 | storage | def storage(self) -> Mapping[str, str]:
request = flask.request
with Timer() as timer:
relayed_response = self.relay(request)
# print("*****************")
# print("STORAGE REQUEST:")
# print(request.get_json())
# print("STORAGE RESPONSE:")
# print(relayed_response.status_code, relayed_response.json())
# print("*****************")
self.snoop_context(request, relayed_response, timer.elapsed)
return relayed_response.json() | python | wandb/testing/relay.py | 706 | 719 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,633 | storage_file | def storage_file(self, path) -> Mapping[str, str]:
request = flask.request
with Timer() as timer:
relayed_response = self.relay(request)
# print("*****************")
# print("STORAGE FILE REQUEST:")
# print("********PATH*********")
# print(path)
# print("********ENDPATH*********")
# print(request.get_json())
# print("STORAGE FILE RESPONSE:")
# print(relayed_response.json())
# print("*****************")
self.snoop_context(request, relayed_response, timer.elapsed, path=path)
return relayed_response.json() | python | wandb/testing/relay.py | 721 | 737 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,634 | control | def control(self) -> Mapping[str, str]:
assert self.relay_control
return self.relay_control.control(flask.request) | python | wandb/testing/relay.py | 739 | 741 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,635 | check_against_limit | def check_against_limit(count, chart, limit=None):
if limit is None:
limit = chart_limit
if count > limit:
warn_chart_limit(limit, chart)
return True
else:
return False | python | wandb/sklearn/utils.py | 14 | 21 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,636 | warn_chart_limit | def warn_chart_limit(limit, chart):
warning = f"using only the first {limit} datapoints to create chart {chart}"
wandb.termwarn(warning) | python | wandb/sklearn/utils.py | 24 | 26 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,637 | encode_labels | def encode_labels(df):
le = sklearn.preprocessing.LabelEncoder()
# apply le on categorical feature columns
categorical_cols = df.select_dtypes(
exclude=["int", "float", "float64", "float32", "int32", "int64"]
).columns
df[categorical_cols] = df[categorical_cols].apply(lambda col: le.fit_transform(col)) | python | wandb/sklearn/utils.py | 29 | 35 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,638 | test_types | def test_types(**kwargs):
test_passed = True
for k, v in kwargs.items():
# check for incorrect types
if (
(k == "X")
or (k == "X_test")
or (k == "y")
or (k == "y_test")
or (k == "y_true")
or (k == "y_probas")
):
# FIXME: do this individually
if not isinstance(
v,
(
Sequence,
Iterable,
np.ndarray,
np.generic,
pd.DataFrame,
pd.Series,
list,
),
):
wandb.termerror("%s is not an array. Please try again." % (k))
test_passed = False
# check for classifier types
if k == "model":
if (not sklearn.base.is_classifier(v)) and (
not sklearn.base.is_regressor(v)
):
wandb.termerror(
"%s is not a classifier or regressor. Please try again." % (k)
)
test_passed = False
elif k == "clf" or k == "binary_clf":
if not (sklearn.base.is_classifier(v)):
wandb.termerror("%s is not a classifier. Please try again." % (k))
test_passed = False
elif k == "regressor":
if not sklearn.base.is_regressor(v):
wandb.termerror("%s is not a regressor. Please try again." % (k))
test_passed = False
elif k == "clusterer":
if not (getattr(v, "_estimator_type", None) == "clusterer"):
wandb.termerror("%s is not a clusterer. Please try again." % (k))
test_passed = False
return test_passed | python | wandb/sklearn/utils.py | 38 | 86 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,639 | test_fitted | def test_fitted(model):
try:
model.predict(np.zeros((7, 3)))
except sklearn.exceptions.NotFittedError:
wandb.termerror("Please fit the model before passing it in.")
return False
except AttributeError:
# Some clustering models (LDA, PCA, Agglomerative) don't implement ``predict``
try:
sklearn.utils.validation.check_is_fitted(
model,
[
"coef_",
"estimator_",
"labels_",
"n_clusters_",
"children_",
"components_",
"n_components_",
"n_iter_",
"n_batch_iter_",
"explained_variance_",
"singular_values_",
"mean_",
],
all_or_any=any,
)
return True
except sklearn.exceptions.NotFittedError:
wandb.termerror("Please fit the model before passing it in.")
return False
except Exception:
# Assume it's fitted, since ``NotFittedError`` wasn't raised
return True | python | wandb/sklearn/utils.py | 89 | 122 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,640 | test_missing | def test_missing(**kwargs):
test_passed = True
for k, v in kwargs.items():
# Missing/empty params/datapoint arrays
if v is None:
wandb.termerror("%s is None. Please try again." % (k))
test_passed = False
if (k == "X") or (k == "X_test"):
if isinstance(v, scipy.sparse.csr.csr_matrix):
v = v.toarray()
elif isinstance(v, (pd.DataFrame, pd.Series)):
v = v.to_numpy()
elif isinstance(v, list):
v = np.asarray(v)
# Warn the user about missing values
missing = 0
missing = np.count_nonzero(pd.isnull(v))
if missing > 0:
wandb.termwarn("%s contains %d missing values. " % (k, missing))
test_passed = False
# Ensure the dataset contains only integers
non_nums = 0
if v.ndim == 1:
non_nums = sum(
1
for val in v
if (
not isinstance(val, (int, float, complex))
and not isinstance(val, np.number)
)
)
else:
non_nums = sum(
1
for sl in v
for val in sl
if (
not isinstance(val, (int, float, complex))
and not isinstance(val, np.number)
)
)
if non_nums > 0:
wandb.termerror(
"%s contains values that are not numbers. Please vectorize, label encode or one hot encode %s and call the plotting function again."
% (k, k)
)
test_passed = False
return test_passed | python | wandb/sklearn/utils.py | 126 | 174 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,641 | round_3 | def round_3(n):
return round(n, 3) | python | wandb/sklearn/utils.py | 177 | 178 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,642 | round_2 | def round_2(n):
return round(n, 2) | python | wandb/sklearn/utils.py | 181 | 182 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,643 | classifier | def classifier(
model,
X_train,
X_test,
y_train,
y_test,
y_pred,
y_probas,
labels,
is_binary=False,
model_name="Classifier",
feature_names=None,
log_learning_curve=False,
):
"""Generate all sklearn classifier plots supported by W&B.
The following plots are generated:
feature importances, confusion matrix, summary metrics,
class propotions, calibration curve, roc curve, precision-recall curve.
Should only be called with a fitted classifer (otherwise an error is thrown).
Arguments:
model: (classifier) Takes in a fitted classifier.
X_train: (arr) Training set features.
y_train: (arr) Training set labels.
X_test: (arr) Test set features.
y_test: (arr) Test set labels.
y_pred: (arr) Test set predictions by the model passed.
y_probas: (arr) Test set predicted probabilities by the model passed.
labels: (list) Named labels for target varible (y). Makes plots easier to
read by replacing target values with corresponding index.
For example if `labels=['dog', 'cat', 'owl']` all 0s are
replaced by dog, 1s by cat.
is_binary: (bool) Is the model passed a binary classifier? Defaults to False
model_name: (str) Model name. Defaults to 'Classifier'
feature_names: (list) Names for features. Makes plots easier to read by
replacing feature indexes with corresponding names.
log_learning_curve: (bool) Whether or not to log the learning curve.
Defaults to False.
Returns:
None: To see plots, go to your W&B run page then expand the 'media' tab
under 'auto visualizations'.
Example:
```python
wandb.sklearn.plot_classifier(
model,
X_train,
X_test,
y_train,
y_test,
y_pred,
y_probas,
["cat", "dog"],
False,
"RandomForest",
["barks", "drools", "plays_fetch", "breed"],
)
```
"""
wandb.termlog("\nPlotting %s." % model_name)
if not isinstance(model, naive_bayes.MultinomialNB):
feature_importances(model, feature_names)
wandb.termlog("Logged feature importances.")
if log_learning_curve:
shared.learning_curve(model, X_train, y_train)
wandb.termlog("Logged learning curve.")
confusion_matrix(y_test, y_pred, labels)
wandb.termlog("Logged confusion matrix.")
shared.summary_metrics(model, X=X_train, y=y_train, X_test=X_test, y_test=y_test)
wandb.termlog("Logged summary metrics.")
class_proportions(y_train, y_test, labels)
wandb.termlog("Logged class proportions.")
if not isinstance(model, naive_bayes.MultinomialNB):
calibration_curve(model, X_train, y_train, model_name)
wandb.termlog("Logged calibration curve.")
roc(y_test, y_probas, labels)
wandb.termlog("Logged roc curve.")
precision_recall(y_test, y_probas, labels)
wandb.termlog("Logged precision-recall curve.") | python | wandb/sklearn/plot/classifier.py | 17 | 106 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,644 | roc | def roc(
y_true=None,
y_probas=None,
labels=None,
plot_micro=True,
plot_macro=True,
classes_to_plot=None,
):
"""Log the receiver-operating characteristic curve.
Arguments:
y_true: (arr) Test set labels.
y_probas: (arr) Test set predicted probabilities.
labels: (list) Named labels for target variable (y). Makes plots easier to
read by replacing target values with corresponding index.
For example if `labels=['dog', 'cat', 'owl']` all 0s are
replaced by dog, 1s by cat.
Returns:
None: To see plots, go to your W&B run page then expand the 'media' tab
under 'auto visualizations'.
Example:
```python
wandb.sklearn.plot_roc(y_true, y_probas, labels)
```
"""
roc_chart = wandb.plots.roc.roc(
y_true, y_probas, labels, plot_micro, plot_macro, classes_to_plot
)
wandb.log({"roc": roc_chart}) | python | wandb/sklearn/plot/classifier.py | 109 | 139 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,645 | confusion_matrix | def confusion_matrix(
y_true=None,
y_pred=None,
labels=None,
true_labels=None,
pred_labels=None,
normalize=False,
):
"""Log a confusion matrix to W&B.
Confusion matrices depict the pattern of misclassifications by a model.
Arguments:
y_true: (arr) Test set labels.
y_probas: (arr) Test set predicted probabilities.
labels: (list) Named labels for target variable (y). Makes plots easier to
read by replacing target values with corresponding index.
For example if `labels=['dog', 'cat', 'owl']` all 0s are
replaced by dog, 1s by cat.
Returns:
None: To see plots, go to your W&B run page then expand the 'media' tab
under 'auto visualizations'.
Example:
```python
wandb.sklearn.plot_confusion_matrix(y_true, y_probas, labels)
```
"""
y_true = np.asarray(y_true)
y_pred = np.asarray(y_pred)
not_missing = utils.test_missing(y_true=y_true, y_pred=y_pred)
correct_types = utils.test_types(y_true=y_true, y_pred=y_pred)
if not_missing and correct_types:
confusion_matrix_chart = calculate.confusion_matrix(
y_true,
y_pred,
labels,
true_labels,
pred_labels,
normalize,
)
wandb.log({"confusion_matrix": confusion_matrix_chart}) | python | wandb/sklearn/plot/classifier.py | 142 | 187 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,646 | precision_recall | def precision_recall(
y_true=None, y_probas=None, labels=None, plot_micro=True, classes_to_plot=None
):
"""Log a precision-recall curve to W&B.
Precision-recall curves depict the tradeoff between positive predictive value (precision)
and true positive rate (recall) as the threshold of a classifier is shifted.
Arguments:
y_true: (arr) Test set labels.
y_probas: (arr) Test set predicted probabilities.
labels: (list) Named labels for target variable (y). Makes plots easier to
read by replacing target values with corresponding index.
For example if `labels=['dog', 'cat', 'owl']` all 0s are
replaced by dog, 1s by cat.
Returns:
None: To see plots, go to your W&B run page then expand the 'media' tab
under 'auto visualizations'.
Example:
```python
wandb.sklearn.plot_precision_recall(y_true, y_probas, labels)
```
"""
precision_recall_chart = wandb.plots.precision_recall(
y_true, y_probas, labels, plot_micro, classes_to_plot
)
wandb.log({"precision_recall": precision_recall_chart}) | python | wandb/sklearn/plot/classifier.py | 190 | 219 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,647 | feature_importances | def feature_importances(
model=None, feature_names=None, title="Feature Importance", max_num_features=50
):
"""Log a plot depicting the relative importance of each feature for a classifier's decisions.
Should only be called with a fitted classifer (otherwise an error is thrown).
Only works with classifiers that have a feature_importances_ attribute, like trees.
Arguments:
model: (clf) Takes in a fitted classifier.
feature_names: (list) Names for features. Makes plots easier to read by
replacing feature indexes with corresponding names.
Returns:
None: To see plots, go to your W&B run page then expand the 'media' tab
under 'auto visualizations'.
Example:
```python
wandb.sklearn.plot_feature_importances(model, ["width", "height", "length"])
```
"""
not_missing = utils.test_missing(model=model)
correct_types = utils.test_types(model=model)
model_fitted = utils.test_fitted(model)
if not_missing and correct_types and model_fitted:
feature_importance_chart = calculate.feature_importances(model, feature_names)
wandb.log({"feature_importances": feature_importance_chart}) | python | wandb/sklearn/plot/classifier.py | 222 | 250 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,648 | class_proportions | def class_proportions(y_train=None, y_test=None, labels=None):
"""Plot the distribution of target classses in training and test sets.
Useful for detecting imbalanced classes.
Arguments:
y_train: (arr) Training set labels.
y_test: (arr) Test set labels.
labels: (list) Named labels for target variable (y). Makes plots easier to
read by replacing target values with corresponding index.
For example if `labels=['dog', 'cat', 'owl']` all 0s are
replaced by dog, 1s by cat.
Returns:
None: To see plots, go to your W&B run page then expand the 'media' tab
under 'auto visualizations'.
Example:
```python
wandb.sklearn.plot_class_proportions(y_train, y_test, ["dog", "cat", "owl"])
```
"""
not_missing = utils.test_missing(y_train=y_train, y_test=y_test)
correct_types = utils.test_types(y_train=y_train, y_test=y_test)
if not_missing and correct_types:
y_train, y_test = np.array(y_train), np.array(y_test)
class_proportions_chart = calculate.class_proportions(y_train, y_test, labels)
wandb.log({"class_proportions": class_proportions_chart}) | python | wandb/sklearn/plot/classifier.py | 253 | 281 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,649 | calibration_curve | def calibration_curve(clf=None, X=None, y=None, clf_name="Classifier"):
"""Log a plot depicting how well-calibrated the predicted probabilities of a classifier are.
Also suggests how to calibrate an uncalibrated classifier. Compares estimated predicted
probabilities by a baseline logistic regression model, the model passed as
an argument, and by both its isotonic calibration and sigmoid calibrations.
The closer the calibration curves are to a diagonal the better.
A sine wave like curve represents an overfitted classifier, while a cosine
wave like curve represents an underfitted classifier.
By training isotonic and sigmoid calibrations of the model and comparing
their curves we can figure out whether the model is over or underfitting and
if so which calibration (sigmoid or isotonic) might help fix this.
For more details, see https://scikit-learn.org/stable/auto_examples/calibration/plot_calibration_curve.html.
Should only be called with a fitted classifer (otherwise an error is thrown).
Please note this function fits variations of the model on the training set when called.
Arguments:
clf: (clf) Takes in a fitted classifier.
X: (arr) Training set features.
y: (arr) Training set labels.
model_name: (str) Model name. Defaults to 'Classifier'
Returns:
None: To see plots, go to your W&B run page then expand the 'media' tab
under 'auto visualizations'.
Example:
```python
wandb.sklearn.plot_calibration_curve(clf, X, y, "RandomForestClassifier")
```
"""
not_missing = utils.test_missing(clf=clf, X=X, y=y)
correct_types = utils.test_types(clf=clf, X=X, y=y)
is_fitted = utils.test_fitted(clf)
if not_missing and correct_types and is_fitted:
y = np.asarray(y)
if y.dtype.char == "U" or not ((y == 0) | (y == 1)).all():
wandb.termwarn(
"This function only supports binary classification at the moment and therefore expects labels to be binary. Skipping calibration curve."
)
return
calibration_curve_chart = calculate.calibration_curves(clf, X, y, clf_name)
wandb.log({"calibration_curve": calibration_curve_chart}) | python | wandb/sklearn/plot/classifier.py | 284 | 330 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,650 | summary_metrics | def summary_metrics(model=None, X=None, y=None, X_test=None, y_test=None):
"""Logs a chart depicting summary metrics for a model.
Should only be called with a fitted model (otherwise an error is thrown).
Arguments:
model: (clf or reg) Takes in a fitted regressor or classifier.
X: (arr) Training set features.
y: (arr) Training set labels.
X_test: (arr) Test set features.
y_test: (arr) Test set labels.
Returns:
None: To see plots, go to your W&B run page then expand the 'media' tab
under 'auto visualizations'.
Example:
```python
wandb.sklearn.plot_summary_metrics(model, X_train, y_train, X_test, y_test)
```
"""
not_missing = utils.test_missing(
model=model, X=X, y=y, X_test=X_test, y_test=y_test
)
correct_types = utils.test_types(
model=model, X=X, y=y, X_test=X_test, y_test=y_test
)
model_fitted = utils.test_fitted(model)
if not_missing and correct_types and model_fitted:
metrics_chart = calculate.summary_metrics(model, X, y, X_test, y_test)
wandb.log({"summary_metrics": metrics_chart}) | python | wandb/sklearn/plot/shared.py | 13 | 44 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,651 | learning_curve | def learning_curve(
model=None,
X=None,
y=None,
cv=None,
shuffle=False,
random_state=None,
train_sizes=None,
n_jobs=1,
scoring=None,
):
"""Logs a plot depicting model performance against dataset size.
Please note this function fits the model to datasets of varying sizes when called.
Arguments:
model: (clf or reg) Takes in a fitted regressor or classifier.
X: (arr) Dataset features.
y: (arr) Dataset labels.
For details on the other keyword arguments, see the documentation for
`sklearn.model_selection.learning_curve`.
Returns:
None: To see plots, go to your W&B run page then expand the 'media' tab
under 'auto visualizations'.
Example:
```python
wandb.sklearn.plot_learning_curve(model, X, y)
```
"""
not_missing = utils.test_missing(model=model, X=X, y=y)
correct_types = utils.test_types(model=model, X=X, y=y)
if not_missing and correct_types:
if train_sizes is None:
train_sizes = np.linspace(0.1, 1.0, 5)
y = np.asarray(y)
learning_curve_chart = calculate.learning_curve(
model, X, y, cv, shuffle, random_state, train_sizes, n_jobs, scoring
)
wandb.log({"learning_curve": learning_curve_chart}) | python | wandb/sklearn/plot/shared.py | 47 | 90 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,652 | regressor | def regressor(model, X_train, X_test, y_train, y_test, model_name="Regressor"):
"""Generates all sklearn regressor plots supported by W&B.
The following plots are generated:
learning curve, summary metrics, residuals plot, outlier candidates.
Should only be called with a fitted regressor (otherwise an error is thrown).
Arguments:
model: (regressor) Takes in a fitted regressor.
X_train: (arr) Training set features.
y_train: (arr) Training set labels.
X_test: (arr) Test set features.
y_test: (arr) Test set labels.
model_name: (str) Model name. Defaults to 'Regressor'
Returns:
None: To see plots, go to your W&B run page then expand the 'media' tab
under 'auto visualizations'.
Example:
```python
wandb.sklearn.plot_regressor(reg, X_train, X_test, y_train, y_test, "Ridge")
```
"""
wandb.termlog("\nPlotting %s." % model_name)
shared.summary_metrics(model, X_train, y_train, X_test, y_test)
wandb.termlog("Logged summary metrics.")
shared.learning_curve(model, X_train, y_train)
wandb.termlog("Logged learning curve.")
outlier_candidates(model, X_train, y_train)
wandb.termlog("Logged outlier candidates.")
residuals(model, X_train, y_train)
wandb.termlog("Logged residuals.") | python | wandb/sklearn/plot/regressor.py | 15 | 52 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,653 | outlier_candidates | def outlier_candidates(regressor=None, X=None, y=None):
"""Measures a datapoint's influence on regression model via cook's distance.
Instances with high influences could potentially be outliers.
Should only be called with a fitted regressor (otherwise an error is thrown).
Please note this function fits the model on the training set when called.
Arguments:
model: (regressor) Takes in a fitted regressor.
X: (arr) Training set features.
y: (arr) Training set labels.
Returns:
None: To see plots, go to your W&B run page then expand the 'media' tab
under 'auto visualizations'.
Example:
```python
wandb.sklearn.plot_outlier_candidates(model, X, y)
```
"""
is_missing = utils.test_missing(regressor=regressor, X=X, y=y)
correct_types = utils.test_types(regressor=regressor, X=X, y=y)
is_fitted = utils.test_fitted(regressor)
if is_missing and correct_types and is_fitted:
y = np.asarray(y)
outliers_chart = calculate.outlier_candidates(regressor, X, y)
wandb.log({"outlier_candidates": outliers_chart}) | python | wandb/sklearn/plot/regressor.py | 55 | 86 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,654 | residuals | def residuals(regressor=None, X=None, y=None):
"""Measures and plots the regressor's predicted value against the residual.
The marginal distribution of residuals is also calculated and plotted.
Should only be called with a fitted regressor (otherwise an error is thrown).
Please note this function fits variations of the model on the training set when called.
Arguments:
regressor: (regressor) Takes in a fitted regressor.
X: (arr) Training set features.
y: (arr) Training set labels.
Returns:
None: To see plots, go to your W&B run page then expand the 'media' tab
under 'auto visualizations'.
Example:
```python
wandb.sklearn.plot_residuals(model, X, y)
```
"""
not_missing = utils.test_missing(regressor=regressor, X=X, y=y)
correct_types = utils.test_types(regressor=regressor, X=X, y=y)
is_fitted = utils.test_fitted(regressor)
if not_missing and correct_types and is_fitted:
y = np.asarray(y)
residuals_chart = calculate.residuals(regressor, X, y)
wandb.log({"residuals": residuals_chart}) | python | wandb/sklearn/plot/regressor.py | 89 | 120 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,655 | clusterer | def clusterer(model, X_train, cluster_labels, labels=None, model_name="Clusterer"):
"""Generates all sklearn clusterer plots supported by W&B.
The following plots are generated:
elbow curve, silhouette plot.
Should only be called with a fitted clusterer (otherwise an error is thrown).
Arguments:
model: (clusterer) Takes in a fitted clusterer.
X_train: (arr) Training set features.
cluster_labels: (list) Names for cluster labels. Makes plots easier to read
by replacing cluster indexes with corresponding names.
labels: (list) Named labels for target varible (y). Makes plots easier to
read by replacing target values with corresponding index.
For example if `labels=['dog', 'cat', 'owl']` all 0s are
replaced by dog, 1s by cat.
model_name: (str) Model name. Defaults to 'Clusterer'
Returns:
None: To see plots, go to your W&B run page then expand the 'media' tab
under 'auto visualizations'.
Example:
```python
wandb.sklearn.plot_clusterer(kmeans, X, cluster_labels, labels, "KMeans")
```
"""
wandb.termlog("\nPlotting %s." % model_name)
if isinstance(model, sklearn.cluster.KMeans):
elbow_curve(model, X_train)
wandb.termlog("Logged elbow curve.")
silhouette(model, X_train, cluster_labels, labels=labels, kmeans=True)
else:
silhouette(model, X_train, cluster_labels, kmeans=False)
wandb.termlog("Logged silhouette plot.") | python | wandb/sklearn/plot/clusterer.py | 14 | 52 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,656 | elbow_curve | def elbow_curve(
clusterer=None, X=None, cluster_ranges=None, n_jobs=1, show_cluster_time=True
):
"""Measures and plots variance explained as a function of the number of clusters.
Useful in picking the optimal number of clusters.
Should only be called with a fitted clusterer (otherwise an error is thrown).
Please note this function fits the model on the training set when called.
Arguments:
model: (clusterer) Takes in a fitted clusterer.
X: (arr) Training set features.
Returns:
None: To see plots, go to your W&B run page then expand the 'media' tab
under 'auto visualizations'.
Example:
```python
wandb.sklearn.plot_elbow_curve(model, X_train)
```
"""
if not hasattr(clusterer, "n_clusters"):
wandb.termlog(
"n_clusters attribute not in classifier. Cannot plot elbow method."
)
return
not_missing = utils.test_missing(clusterer=clusterer)
correct_types = utils.test_types
is_fitted = utils.test_fitted(clusterer)
if not_missing and correct_types and is_fitted:
elbow_curve_chart = calculate.elbow_curve(
clusterer, X, cluster_ranges, n_jobs, show_cluster_time
)
wandb.log({"elbow_curve": elbow_curve_chart}) | python | wandb/sklearn/plot/clusterer.py | 55 | 94 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,657 | silhouette | def silhouette(
clusterer=None,
X=None,
cluster_labels=None,
labels=None,
metric="euclidean",
kmeans=True,
):
"""Measures & plots silhouette coefficients.
Silhouette coefficients near +1 indicate that the sample is far away from
the neighboring clusters. A value near 0 indicates that the sample is on or
very close to the decision boundary between two neighboring clusters and
negative values indicate that the samples might have been assigned to the wrong cluster.
Should only be called with a fitted clusterer (otherwise an error is thrown).
Please note this function fits the model on the training set when called.
Arguments:
model: (clusterer) Takes in a fitted clusterer.
X: (arr) Training set features.
cluster_labels: (list) Names for cluster labels. Makes plots easier to read
by replacing cluster indexes with corresponding names.
Returns:
None: To see plots, go to your W&B run page then expand the 'media' tab
under 'auto visualizations'.
Example:
```python
wandb.sklearn.plot_silhouette(model, X_train, ["spam", "not spam"])
```
"""
not_missing = utils.test_missing(clusterer=clusterer)
correct_types = utils.test_types(clusterer=clusterer)
is_fitted = utils.test_fitted(clusterer)
if not_missing and correct_types and is_fitted:
if isinstance(X, (pd.DataFrame)):
X = X.values
silhouette_chart = calculate.silhouette(
clusterer, X, cluster_labels, labels, metric, kmeans
)
wandb.log({"silhouette_plot": silhouette_chart}) | python | wandb/sklearn/plot/clusterer.py | 97 | 141 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,658 | learning_curve | def learning_curve(
model,
X,
y,
cv=None,
shuffle=False,
random_state=None,
train_sizes=None,
n_jobs=1,
scoring=None,
):
"""Train model on datasets of varying size and generates plot of score vs size.
Called by plot_learning_curve to visualize learning curve. Please use the function
plot_learning_curve() if you wish to visualize your learning curves.
"""
train_sizes, train_scores, test_scores = model_selection.learning_curve(
model,
X,
y,
cv=cv,
n_jobs=n_jobs,
train_sizes=train_sizes,
scoring=scoring,
shuffle=shuffle,
random_state=random_state,
)
train_scores_mean = np.mean(train_scores, axis=1)
test_scores_mean = np.mean(test_scores, axis=1)
table = make_table(train_scores_mean, test_scores_mean, train_sizes)
chart = wandb.visualize("wandb/learning_curve/v1", table)
return chart | python | wandb/sklearn/calculate/learning_curve.py | 13 | 46 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,659 | make_table | def make_table(train, test, train_sizes):
data = []
for i in range(len(train)):
if utils.check_against_limit(
i,
"learning_curve",
utils.chart_limit / 2,
):
break
train_set = ["train", utils.round_2(train[i]), train_sizes[i]]
test_set = ["test", utils.round_2(test[i]), train_sizes[i]]
data.append(train_set)
data.append(test_set)
table = wandb.Table(columns=["dataset", "score", "train_size"], data=data)
return table | python | wandb/sklearn/calculate/learning_curve.py | 49 | 64 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,660 | class_proportions | def class_proportions(y_train, y_test, labels):
# Get the unique values from the dataset
targets = (y_train,) if y_test is None else (y_train, y_test)
class_ids = np.array(unique_labels(*targets))
# Compute the class counts
counts_train = np.array([(y_train == c).sum() for c in class_ids])
counts_test = np.array([(y_test == c).sum() for c in class_ids])
class_column, dataset_column, count_column = make_columns(
class_ids, counts_train, counts_test
)
if labels is not None and (
isinstance(class_column[0], int) or isinstance(class_column[0], np.integer)
):
class_column = get_named_labels(labels, class_column)
table = make_table(class_column, dataset_column, count_column)
chart = wandb.visualize("wandb/class_proportions/v1", table)
return chart | python | wandb/sklearn/calculate/class_proportions.py | 13 | 34 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,661 | make_table | def make_table(class_column, dataset_column, count_column):
columns = ["class", "dataset", "count"]
data = list(zip(class_column, dataset_column, count_column))
return wandb.Table(data=data, columns=columns) | python | wandb/sklearn/calculate/class_proportions.py | 37 | 41 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,662 | make_columns | def make_columns(class_ids, counts_train, counts_test):
class_column, dataset_column, count_column = [], [], []
for i in range(len(class_ids)):
# add class counts from training set
class_column.append(class_ids[i])
dataset_column.append("train")
count_column.append(counts_train[i])
# add class counts from test set
class_column.append(class_ids[i])
dataset_column.append("test")
count_column.append(counts_test[i])
if utils.check_against_limit(
i,
"class_proportions",
utils.chart_limit,
):
break
return class_column, dataset_column, count_column | python | wandb/sklearn/calculate/class_proportions.py | 44 | 64 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,663 | get_named_labels | def get_named_labels(labels, numeric_labels):
return np.array([labels[num_label] for num_label in numeric_labels]) | python | wandb/sklearn/calculate/class_proportions.py | 67 | 68 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,664 | silhouette | def silhouette(clusterer, X, cluster_labels, labels, metric, kmeans):
# Run clusterer for n_clusters in range(len(cluster_ranges), get cluster labels
# TODO - keep/delete once we decide if we should train clusterers
# or ask for trained models
# clusterer.set_params(n_clusters=n_clusters, random_state=42)
# cluster_labels = clusterer.fit_predict(X)
cluster_labels = np.asarray(cluster_labels)
labels = np.asarray(labels)
le = LabelEncoder()
_ = le.fit_transform(cluster_labels)
n_clusters = len(np.unique(cluster_labels))
# The silhouette_score gives the average value for all the samples.
# This gives a perspective into the density and separation of the formed
# clusters
silhouette_avg = silhouette_score(X, cluster_labels, metric=metric)
# Compute the silhouette scores for each sample
sample_silhouette_values = silhouette_samples(X, cluster_labels, metric=metric)
x_sil, y_sil, color_sil = [], [], []
count, y_lower = 0, 10
for i in range(n_clusters):
# Aggregate the silhouette scores for samples belonging to
# cluster i, and sort them
ith_cluster_silhouette_values = sample_silhouette_values[cluster_labels == i]
ith_cluster_silhouette_values.sort()
size_cluster_i = ith_cluster_silhouette_values.shape[0]
y_upper = y_lower + size_cluster_i
y_values = np.arange(y_lower, y_upper)
for j in range(len(y_values)):
y_sil.append(y_values[j])
x_sil.append(ith_cluster_silhouette_values[j])
color_sil.append(i)
count += 1
if utils.check_against_limit(count, "silhouette", utils.chart_limit):
break
# Compute the new y_lower for next plot
y_lower = y_upper + 10 # 10 for the 0 samples
if kmeans:
centers = clusterer.cluster_centers_
centerx = centers[:, 0]
centery = centers[:, 1]
else:
centerx = [None] * len(color_sil)
centery = [None] * len(color_sil)
table = make_table(
X[:, 0],
X[:, 1],
cluster_labels,
centerx,
centery,
y_sil,
x_sil,
color_sil,
silhouette_avg,
)
chart = wandb.visualize("wandb/silhouette_/v1", table)
return chart | python | wandb/sklearn/calculate/silhouette.py | 14 | 83 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,665 | make_table | def make_table(x, y, colors, centerx, centery, y_sil, x_sil, color_sil, silhouette_avg):
columns = [
"x",
"y",
"colors",
"centerx",
"centery",
"y_sil",
"x1",
"x2",
"color_sil",
"silhouette_avg",
]
data = [
[
x[i],
y[i],
colors[i],
centerx[colors[i]],
centery[colors[i]],
y_sil[i],
0,
x_sil[i],
color_sil[i],
silhouette_avg,
]
for i in range(len(color_sil))
]
table = wandb.Table(data=data, columns=columns)
return table | python | wandb/sklearn/calculate/silhouette.py | 86 | 118 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,666 | calibration_curves | def calibration_curves(clf, X, y, clf_name):
# ComplementNB (introduced in 0.20.0) requires non-negative features
if int(sklearn.__version__.split(".")[1]) >= 20 and isinstance(
clf, naive_bayes.ComplementNB
):
X = X - X.min()
# Calibrated with isotonic calibration
isotonic = CalibratedClassifierCV(clf, cv=2, method="isotonic")
# Calibrated with sigmoid calibration
sigmoid = CalibratedClassifierCV(clf, cv=2, method="sigmoid")
# Logistic regression with no calibration as baseline
lr = LogisticRegression(C=1.0)
model_column = [] # color
frac_positives_column = [] # y axis
mean_pred_value_column = [] # x axis
hist_column = [] # barchart y
edge_column = [] # barchart x
# Add curve for perfectly calibrated model
# format: model, fraction_of_positives, mean_predicted_value
model_column.append("Perfectly calibrated")
frac_positives_column.append(0)
mean_pred_value_column.append(0)
hist_column.append(0)
edge_column.append(0)
model_column.append("Perfectly calibrated")
hist_column.append(0)
edge_column.append(0)
frac_positives_column.append(1)
mean_pred_value_column.append(1)
X_train, X_test, y_train, y_test = model_selection.train_test_split(
X, y, test_size=0.9, random_state=42
)
# Add curve for LogisticRegression baseline and other models
models = [lr, isotonic, sigmoid]
names = ["Logistic", f"{clf_name} Isotonic", f"{clf_name} Sigmoid"]
for model, name in zip(models, names):
model.fit(X_train, y_train)
if hasattr(model, "predict_proba"):
prob_pos = model.predict_proba(X_test)[:, 1]
else: # use decision function
prob_pos = model.decision_function(X_test)
prob_pos = (prob_pos - prob_pos.min()) / (prob_pos.max() - prob_pos.min())
hist, edges = np.histogram(prob_pos, bins=10, density=False)
frac_positives, mean_pred_value = sklearn.calibration.calibration_curve(
y_test, prob_pos, n_bins=10
)
# format: model, fraction_of_positives, mean_predicted_value
num_entries = len(frac_positives)
for i in range(num_entries):
hist_column.append(hist[i])
edge_column.append(edges[i])
model_column.append(name)
frac_positives_column.append(utils.round_3(frac_positives[i]))
mean_pred_value_column.append(utils.round_3(mean_pred_value[i]))
if utils.check_against_limit(
i,
"calibration_curve",
utils.chart_limit - 2,
):
break
table = make_table(
model_column,
frac_positives_column,
mean_pred_value_column,
hist_column,
edge_column,
)
chart = wandb.visualize("wandb/calibration/v1", table)
return chart | python | wandb/sklearn/calculate/calibration_curves.py | 16 | 98 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,667 | make_table | def make_table(
model_column,
frac_positives_column,
mean_pred_value_column,
hist_column,
edge_column,
):
columns = [
"model",
"fraction_of_positives",
"mean_predicted_value",
"hist_dict",
"edge_dict",
]
data = list(
zip(
model_column,
frac_positives_column,
mean_pred_value_column,
hist_column,
edge_column,
)
)
return wandb.Table(columns=columns, data=data) | python | wandb/sklearn/calculate/calibration_curves.py | 101 | 126 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,668 | outlier_candidates | def outlier_candidates(regressor, X, y):
# Fit a linear model to X and y to compute MSE
regressor.fit(X, y)
# Leverage is computed as the diagonal of the projection matrix of X
leverage = (X * np.linalg.pinv(X).T).sum(1)
# Compute the rank and the degrees of freedom of the OLS model
rank = np.linalg.matrix_rank(X)
df = X.shape[0] - rank
# Compute the MSE from the residuals
residuals = y - regressor.predict(X)
mse = np.dot(residuals, residuals) / df
# Compute Cook's distance
residuals_studentized = residuals / np.sqrt(mse) / np.sqrt(1 - leverage)
distance_ = residuals_studentized**2 / X.shape[1]
distance_ *= leverage / (1 - leverage)
# Compute the influence threshold rule of thumb
influence_threshold_ = 4 / X.shape[0]
outlier_percentage_ = sum(distance_ >= influence_threshold_) / X.shape[0]
outlier_percentage_ *= 100.0
distance_dict, count = [], 0
for d in distance_:
distance_dict.append(d)
count += 1
if utils.check_against_limit(
count,
"outlier_candidates",
utils.chart_limit,
):
break
table = make_table(distance_dict, outlier_percentage_, influence_threshold_)
chart = wandb.visualize("wandb/outliers/v1", table)
return chart | python | wandb/sklearn/calculate/outlier_candidates.py | 12 | 51 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,669 | make_table | def make_table(distance, outlier_percentage, influence_threshold):
columns = [
"distance",
"instance_indicies",
"outlier_percentage",
"influence_threshold",
]
data = [
[distance[i], i, utils.round_3(outlier_percentage), influence_threshold]
for i in range(len(distance))
]
table = wandb.Table(columns=columns, data=data)
return table | python | wandb/sklearn/calculate/outlier_candidates.py | 54 | 69 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,670 | residuals | def residuals(regressor, X, y):
# Create the train and test splits
X_train, X_test, y_train, y_test = model_selection.train_test_split(
X, y, test_size=0.2
)
# Store labels and colors for the legend ordered by call
regressor.fit(X_train, y_train)
train_score_ = regressor.score(X_train, y_train)
test_score_ = regressor.score(X_test, y_test)
y_pred_train = regressor.predict(X_train)
residuals_train = y_pred_train - y_train
y_pred_test = regressor.predict(X_test)
residuals_test = y_pred_test - y_test
table = make_table(
y_pred_train,
residuals_train,
y_pred_test,
residuals_test,
train_score_,
test_score_,
)
chart = wandb.visualize("wandb/residuals_plot/v1", table)
return chart | python | wandb/sklearn/calculate/residuals.py | 12 | 39 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,671 | make_table | def make_table(
y_pred_train,
residuals_train,
y_pred_test,
residuals_test,
train_score_,
test_score_,
):
y_pred_column, dataset_column, residuals_column = [], [], []
datapoints, max_datapoints_train = 0, 100
for pred, residual in zip(y_pred_train, residuals_train):
# add class counts from training set
y_pred_column.append(pred)
dataset_column.append("train")
residuals_column.append(residual)
datapoints += 1
if utils.check_against_limit(datapoints, "residuals", max_datapoints_train):
break
datapoints = 0
for pred, residual in zip(y_pred_test, residuals_test):
# add class counts from training set
y_pred_column.append(pred)
dataset_column.append("test")
residuals_column.append(residual)
datapoints += 1
if utils.check_against_limit(datapoints, "residuals", max_datapoints_train):
break
columns = ["dataset", "y_pred", "residuals", "train_score", "test_score"]
data = [
[
dataset_column[i],
y_pred_column[i],
residuals_column[i],
train_score_,
test_score_,
]
for i in range(len(y_pred_column))
]
table = wandb.Table(columns=columns, data=data)
return table | python | wandb/sklearn/calculate/residuals.py | 42 | 86 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,672 | decision_boundaries | def decision_boundaries(
decision_boundary_x,
decision_boundary_y,
decision_boundary_color,
train_x,
train_y,
train_color,
test_x,
test_y,
test_color,
):
x_dict, y_dict, color_dict = [], [], []
for i in range(min(len(decision_boundary_x), 100)):
x_dict.append(decision_boundary_x[i])
y_dict.append(decision_boundary_y[i])
color_dict.append(decision_boundary_color)
for i in range(300):
x_dict.append(test_x[i])
y_dict.append(test_y[i])
color_dict.append(test_color[i])
for i in range(min(len(train_x), 600)):
x_dict.append(train_x[i])
y_dict.append(train_y[i])
color_dict.append(train_color[i])
return wandb.visualize(
"wandb/decision_boundaries/v1",
wandb.Table(
columns=["x", "y", "color"],
data=[[x_dict[i], y_dict[i], color_dict[i]] for i in range(len(x_dict))],
),
) | python | wandb/sklearn/calculate/decision_boundaries.py | 9 | 40 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,673 | elbow_curve | def elbow_curve(clusterer, X, cluster_ranges, n_jobs, show_cluster_time):
if cluster_ranges is None:
cluster_ranges = range(1, 10, 2)
else:
cluster_ranges = sorted(cluster_ranges)
clfs, times = _compute_results_parallel(n_jobs, clusterer, X, cluster_ranges)
clfs = np.absolute(clfs)
table = make_table(cluster_ranges, clfs, times)
chart = wandb.visualize("wandb/elbow/v1", table)
return chart | python | wandb/sklearn/calculate/elbow_curve.py | 14 | 27 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,674 | make_table | def make_table(cluster_ranges, clfs, times):
columns = ["cluster_ranges", "errors", "clustering_time"]
data = list(zip(cluster_ranges, clfs, times))
table = wandb.Table(columns=columns, data=data)
return table | python | wandb/sklearn/calculate/elbow_curve.py | 30 | 37 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,675 | _compute_results_parallel | def _compute_results_parallel(n_jobs, clusterer, X, cluster_ranges):
parallel_runner = Parallel(n_jobs=n_jobs)
_cluster_scorer = delayed(_clone_and_score_clusterer)
results = parallel_runner(_cluster_scorer(clusterer, X, i) for i in cluster_ranges)
clfs, times = zip(*results)
return clfs, times | python | wandb/sklearn/calculate/elbow_curve.py | 40 | 47 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,676 | _clone_and_score_clusterer | def _clone_and_score_clusterer(clusterer, X, n_clusters):
start = time.time()
clusterer = clone(clusterer)
setattr(clusterer, "n_clusters", n_clusters)
return clusterer.fit(X).score(X), time.time() - start | python | wandb/sklearn/calculate/elbow_curve.py | 50 | 55 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,677 | summary_metrics | def summary_metrics(model=None, X=None, y=None, X_test=None, y_test=None):
"""Calculate summary metrics for both regressors and classifiers.
Called by plot_summary_metrics to visualize metrics. Please use the function
plot_summary_metrics() if you wish to visualize your summary metrics.
"""
y, y_test = np.asarray(y), np.asarray(y_test)
metrics = {}
model_name = model.__class__.__name__
y_pred = model.predict(X_test)
if sklearn.base.is_classifier(model):
accuracy_score = sklearn.metrics.accuracy_score(y_test, y_pred)
metrics["accuracy_score"] = accuracy_score
precision = sklearn.metrics.precision_score(y_test, y_pred, average="weighted")
metrics["precision"] = precision
recall = sklearn.metrics.recall_score(y_test, y_pred, average="weighted")
metrics["recall"] = recall
f1_score = sklearn.metrics.f1_score(y_test, y_pred, average="weighted")
metrics["f1_score"] = f1_score
elif sklearn.base.is_regressor(model):
mae = sklearn.metrics.mean_absolute_error(y_test, y_pred)
metrics["mae"] = mae
mse = sklearn.metrics.mean_squared_error(y_test, y_pred)
metrics["mse"] = mse
r2_score = sklearn.metrics.r2_score(y_test, y_pred)
metrics["r2_score"] = r2_score
metrics = {name: utils.round_2(metric) for name, metric in metrics.items()}
table = make_table(metrics, model_name)
chart = wandb.visualize("wandb/metrics/v1", table)
return chart | python | wandb/sklearn/calculate/summary_metrics.py | 13 | 53 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,678 | make_table | def make_table(metrics, model_name):
columns = ["metric_name", "metric_value", "model_name"]
table_content = [[name, value, model_name] for name, value in metrics.items()]
table = wandb.Table(columns=columns, data=table_content)
return table | python | wandb/sklearn/calculate/summary_metrics.py | 56 | 62 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,679 | feature_importances | def feature_importances(model, feature_names):
attributes_to_check = ["feature_importances_", "feature_log_prob_", "coef_"]
found_attribute = check_for_attribute_on(model, attributes_to_check)
if found_attribute is None:
wandb.termwarn(
f"could not find any of attributes {', '.join(attributes_to_check)} on classifier. Cannot plot feature importances."
)
return
elif found_attribute == "feature_importances_":
importances = model.feature_importances_
elif found_attribute == "coef_": # ElasticNet-like models
importances = model.coef_
elif found_attribute == "feature_log_prob_":
# coef_ was deprecated in sklearn 0.24, replaced with
# feature_log_prob_
importances = model.feature_log_prob_
if len(importances.shape) > 1:
n_significant_dims = sum(i > 1 for i in importances.shape)
if n_significant_dims > 1:
nd = len(importances.shape)
wandb.termwarn(
f"{nd}-dimensional feature importances array passed to plot_feature_importances. "
f"{nd}-dimensional and higher feature importances arrays are not currently supported. "
f"These importances will not be plotted."
)
return
else:
importances = np.squeeze(importances)
indices = np.argsort(importances)[::-1]
importances = importances[indices]
if feature_names is None:
feature_names = indices
else:
feature_names = np.array(feature_names)[indices]
table = make_table(feature_names, importances)
chart = wandb.visualize("wandb/feature_importances/v1", table)
return chart | python | wandb/sklearn/calculate/feature_importances.py | 11 | 52 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,680 | make_table | def make_table(feature_names, importances):
table = wandb.Table(
columns=["feature_names", "importances"],
data=[[feature_names[i], importances[i]] for i in range(len(feature_names))],
)
return table | python | wandb/sklearn/calculate/feature_importances.py | 55 | 60 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,681 | check_for_attribute_on | def check_for_attribute_on(model, attributes_to_check):
for attr in attributes_to_check:
if hasattr(model, attr):
return attr
return None | python | wandb/sklearn/calculate/feature_importances.py | 63 | 67 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,682 | validate_labels | def validate_labels(*args, **kwargs): # FIXME
assert False | python | wandb/sklearn/calculate/confusion_matrix.py | 15 | 16 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,683 | confusion_matrix | def confusion_matrix(
y_true=None,
y_pred=None,
labels=None,
true_labels=None,
pred_labels=None,
normalize=False,
):
"""Compute the confusion matrix to evaluate the performance of a classification.
Called by plot_confusion_matrix to visualize roc curves. Please use the function
plot_confusion_matrix() if you wish to visualize your confusion matrix.
"""
cm = metrics.confusion_matrix(y_true, y_pred)
if labels is None:
classes = unique_labels(y_true, y_pred)
else:
classes = np.asarray(labels)
if normalize:
cm = cm.astype("float") / cm.sum(axis=1)[:, np.newaxis]
cm = np.around(cm, decimals=2)
cm[np.isnan(cm)] = 0.0
if true_labels is None:
true_classes = classes
else:
validate_labels(classes, true_labels, "true_labels")
true_label_indexes = np.in1d(classes, true_labels)
true_classes = classes[true_label_indexes]
cm = cm[true_label_indexes]
if pred_labels is None:
pred_classes = classes
else:
validate_labels(classes, pred_labels, "pred_labels")
pred_label_indexes = np.in1d(classes, pred_labels)
pred_classes = classes[pred_label_indexes]
cm = cm[:, pred_label_indexes]
table = make_table(cm, pred_classes, true_classes, labels)
chart = wandb.visualize("wandb/confusion_matrix/v1", table)
return chart | python | wandb/sklearn/calculate/confusion_matrix.py | 19 | 67 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,684 | make_table | def make_table(cm, pred_classes, true_classes, labels):
data, count = [], 0
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
if labels is not None and (
isinstance(pred_classes[i], int) or isinstance(pred_classes[0], np.integer)
):
pred = labels[pred_classes[i]]
true = labels[true_classes[j]]
else:
pred = pred_classes[i]
true = true_classes[j]
data.append([pred, true, cm[i, j]])
count += 1
if utils.check_against_limit(
count,
"confusion_matrix",
utils.chart_limit,
):
break
table = wandb.Table(columns=["Predicted", "Actual", "Count"], data=data)
return table | python | wandb/sklearn/calculate/confusion_matrix.py | 70 | 92 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,685 | __init__ | def __init__(self):
object.__setattr__(self, "_items", dict())
object.__setattr__(self, "_locked", dict())
object.__setattr__(self, "_users", dict())
object.__setattr__(self, "_users_inv", dict())
object.__setattr__(self, "_users_cnt", 0)
object.__setattr__(self, "_callback", None)
object.__setattr__(self, "_settings", None)
object.__setattr__(self, "_artifact_callback", None)
self._load_defaults() | python | wandb/sdk/wandb_config.py | 95 | 105 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,686 | _set_callback | def _set_callback(self, cb):
object.__setattr__(self, "_callback", cb) | python | wandb/sdk/wandb_config.py | 107 | 108 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,687 | _set_artifact_callback | def _set_artifact_callback(self, cb):
object.__setattr__(self, "_artifact_callback", cb) | python | wandb/sdk/wandb_config.py | 110 | 111 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,688 | _set_settings | def _set_settings(self, settings):
object.__setattr__(self, "_settings", settings) | python | wandb/sdk/wandb_config.py | 113 | 114 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,689 | __repr__ | def __repr__(self):
return str(dict(self)) | python | wandb/sdk/wandb_config.py | 116 | 117 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,690 | keys | def keys(self):
return [k for k in self._items.keys() if not k.startswith("_")] | python | wandb/sdk/wandb_config.py | 119 | 120 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,691 | _as_dict | def _as_dict(self):
return self._items | python | wandb/sdk/wandb_config.py | 122 | 123 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,692 | as_dict | def as_dict(self):
# TODO: add telemetry, deprecate, then remove
return dict(self) | python | wandb/sdk/wandb_config.py | 125 | 127 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,693 | __getitem__ | def __getitem__(self, key):
return self._items[key] | python | wandb/sdk/wandb_config.py | 129 | 130 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,694 | _check_locked | def _check_locked(self, key, ignore_locked=False) -> bool:
locked = self._locked.get(key)
if locked is not None:
locked_user = self._users_inv[locked]
if not ignore_locked:
wandb.termwarn(
"Config item '%s' was locked by '%s' (ignored update)."
% (key, locked_user)
)
return True
return False | python | wandb/sdk/wandb_config.py | 132 | 142 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,695 | __setitem__ | def __setitem__(self, key, val):
if self._check_locked(key):
return
with wandb.sdk.lib.telemetry.context() as tel:
tel.feature.set_config_item = True
self._raise_value_error_on_nested_artifact(val, nested=True)
key, val = self._sanitize(key, val)
self._items[key] = val
logger.info("config set %s = %s - %s", key, val, self._callback)
if self._callback:
self._callback(key=key, val=val) | python | wandb/sdk/wandb_config.py | 144 | 154 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,696 | items | def items(self):
return [(k, v) for k, v in self._items.items() if not k.startswith("_")] | python | wandb/sdk/wandb_config.py | 156 | 157 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,697 | __getattr__ | def __getattr__(self, key):
try:
return self.__getitem__(key)
except KeyError as ke:
raise AttributeError(
f"{self.__class__!r} object has no attribute {key!r}"
) from ke | python | wandb/sdk/wandb_config.py | 161 | 167 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,698 | __contains__ | def __contains__(self, key):
return key in self._items | python | wandb/sdk/wandb_config.py | 169 | 170 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,699 | _update | def _update(self, d, allow_val_change=None, ignore_locked=None):
parsed_dict = wandb_helper.parse_config(d)
locked_keys = set()
for key in list(parsed_dict):
if self._check_locked(key, ignore_locked=ignore_locked):
locked_keys.add(key)
sanitized = self._sanitize_dict(
parsed_dict, allow_val_change, ignore_keys=locked_keys
)
self._items.update(sanitized)
return sanitized | python | wandb/sdk/wandb_config.py | 172 | 182 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
2,700 | update | def update(self, d, allow_val_change=None):
sanitized = self._update(d, allow_val_change)
if self._callback:
self._callback(data=sanitized) | python | wandb/sdk/wandb_config.py | 184 | 187 | {
"name": "Git-abouvier/wandb",
"url": "https://github.com/Git-abouvier/wandb.git",
"license": "MIT",
"stars": 0,
"forks": 0
} |
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