async def model_predict(self, request, mctx):
# TODO Provide an iterkey method for model prediction
chunk_size = int(request.match_info["chunk_size"])
if chunk_size != 0:
return web.json_response(
{"error": "Multiple request iteration not yet supported"},
status=HTTPStatus.BAD_REQUEST,
)
# Get the records
records: Dict[str, Record] = {}
# Create a source with will provide the records
async with Sources(
MemorySource(records=[
Record(key, data=record_data)
for key, record_data in (await request.json()).items()
])) as source:
async with source() as sctx:
# Feed them through prediction
return web.json_response({
"iterkey": None,
"records": {
record.key: record.export()
async for record in mctx.predict(sctx)
},
})
async def accuracy(self, sources: Sources) -> Accuracy:
# Load saved regression line
regression_line = self.storage.get("regression_line", None)
# Ensure the model has been trained before we try to make a prediction
if regression_line is None:
raise ModelNotTrained("Train model before assessing for accuracy")
# Split regression line tuple into variables, ignore accuracy from
# training data since we'll be re-calculating it for the test data
m, b, _accuracy = regression_line
# X and Y data
x = []
y = []
# Go through all records that have the feature we're testing on and the
# feature we want to predict.
async for record in sources.with_features(
[self.config.feature.name, self.config.predict.name]):
x.append(record.feature(self.config.feature.name))
y.append(record.feature(self.config.predict.name))
# Use self.logger to report how many records are being used for testing
self.logger.debug("Number of test records: %d", len(x))
# Calculate the regression line for test data and accuracy of line
regression_line = [m * x + b for x in x]
accuracy = coeff_of_deter(y, regression_line)
# Update the accuracy to be the accuracy when assessed on the test data
self.storage["regression_line"] = m, b, accuracy
return Accuracy(accuracy)
async def train(self, sources: Sources):
async for record in sources.with_features(self.features +
[self.config.predict.NAME]):
feature_data = record.features(self.features +
[self.config.predict.NAME])
self.xData = np.append(self.xData, feature_data[self.features[0]])
self.yData = np.append(self.yData,
feature_data[self.config.predict.NAME])
self.separating_line = self.best_separating_line()
class ServerConfig(TLSCMDConfig, MultiCommCMDConfig):
port: int = field(
"Port to bind to", default=8080,
)
addr: str = field(
"Address to bind to", default="127.0.0.1",
)
upload_dir: str = field(
"Directory to store uploaded files in", default=None,
)
static: str = field(
"Directory to serve static content from", default=None,
)
js: bool = field(
"Serve JavaScript API file at /api.js",
default=False,
action="store_true",
)
insecure: bool = field(
"Start without TLS encryption", action="store_true", default=False,
)
cors_domains: List[str] = field(
"Domains to allow CORS for (see keys in defaults dict for aiohttp_cors.setup)",
default_factory=lambda: [],
)
allow_caching: bool = field(
"Allow caching of HTTP responses", action="store_true", default=False,
)
models: Model = field(
"Models configured on start",
default_factory=lambda: AsyncContextManagerList(),
action=list_action(AsyncContextManagerList),
labeled=True,
)
sources: Sources = field(
"Sources configured on start",
default_factory=lambda: Sources(),
action=list_action(Sources),
labeled=True,
)
scorers: AccuracyScorer = field(
"Scorers configured on start",
default_factory=lambda: AsyncContextManagerList(),
action=list_action(AsyncContextManagerList),
labeled=True,
)
redirect: List[str] = field(
"list of METHOD SOURCE_PATH DESTINATION_PATH pairs, number of elements must be divisible by 3",
action=ParseRedirectsAction,
default_factory=lambda: [],
)
portfile: pathlib.Path = field(
"File to write bound port to when starting. Helpful when port 0 was requeseted to bind to any free port",
default=None,
)
async def train(self, sources: Sources) -> None:
async for record in sources.with_features(
self.features + [self.parent.config.predict.name]):
feature_data = record.features(self.features +
[self.parent.config.predict.name])
df = self.pd.DataFrame(feature_data, index=[0])
xdata = df.drop([self.parent.config.predict.name], 1)
ydata = df[self.parent.config.predict.name]
self.lm.compute(xdata, ydata)
self.lm_trained = self.lm.finalize().model
self.joblib.dump(self.lm_trained, self.path)
async def train(self, sources: Sources):
all_data = []
async for record in sources.with_features(
self.features + [self.parent.config.predict.name]):
all_data.append(record.features())
df = pd.DataFrame(all_data)
y_train = df[[self.parent.config.predict.name]]
x_train = df.drop(columns=[self.parent.config.predict.name])
self.model.fit(x_train, y_train)
self.model.fit_ensemble(y_train,
ensemble_size=self.parent.config.ensemble_size)
joblib.dump(self.model, self.path)
async def accuracy(self, sources: Sources) -> Accuracy:
if not self.model:
raise ModelNotTrained("Train the model before assessing accuracy")
test_data = []
async for record in sources.with_features(
self.features + [self.parent.config.predict.name]):
test_data.append(record.features())
df = pd.DataFrame(test_data)
y_test = df[[self.parent.config.predict.name]]
x_test = df.drop(columns=[self.parent.config.predict.name])
predictions = await self.get_predictions(x_test)
accuracy = await self.accuracy_score(y_test, predictions)
return Accuracy(accuracy)
async def train(self, sources: Sources) -> None:
async for record in sources.with_features(
self.features + [self.parent.config.predict.name]):
feature_data = record.features(self.features +
[self.parent.config.predict.name])
# NOTE Duplicate feature data due to regression in oneDAL
# See https://github.com/intel/dffml/issues/801
df = self.pd.DataFrame([feature_data] * 2, index=[0, 1])
xdata = df.drop([self.parent.config.predict.name], 1)
ydata = df[self.parent.config.predict.name]
self.lm.compute(xdata, ydata)
self.lm_trained = self.lm.finalize().model
self.joblib.dump(self.lm_trained, self.path)
async def train(self, sources: Sources) -> None:
# X and Y data
x = []
y = []
# Go through all records that have the feature we're training on and the
# feature we want to predict.
async for record in sources.with_features(
[self.config.feature.name, self.config.predict.name]):
x.append(record.feature(self.config.feature.name))
y.append(record.feature(self.config.predict.name))
# Use self.logger to report how many records are being used for training
self.logger.debug("Number of training records: %d", len(x))
# Save m, b, and accuracy
self.storage["regression_line"] = best_fit_line(x, y)
async def train(self, sources: Sources) -> None:
# X and Y data
x = []
y = []
# Go through all records that have the feature we're training on and the
# feature we want to predict. Since our model only supports 1 feature,
# the self.features list will only have one element at index 0.
async for record in sources.with_features(self.features +
[self.config.predict.NAME]):
x.append(record.feature(self.features[0]))
y.append(record.feature(self.config.predict.NAME))
# Use self.logger to report how many records are being used for training
self.logger.debug("Number of input records: %d", len(x))
# Save m, b, and accuracy
self.storage["regression_line"] = best_fit_line(x, y)
async def accuracy(self, sources: Sources) -> Accuracy:
if self.lm_trained is None:
raise ModelNotTrained("Train model before assessing for accuracy.")
feature_data = []
async for record in sources.with_features(
self.features + [self.parent.config.predict.name]):
feature_data.append(
record.features(self.features +
[self.parent.config.predict.name]))
df = self.pd.DataFrame(feature_data)
xdata = df.drop([self.parent.config.predict.name], 1)
ydata = df[self.parent.config.predict.name]
preds = self.ac_predictor.compute(xdata, self.lm_trained)
# Calculate accuracy with an error margin of 0.1
accuracy_val = sum(
self.compare(list(map(abs, map(sub, ydata, preds.prediction))),
0.1)) / len(ydata)
return Accuracy(accuracy_val)
class ServerConfig(TLSCMDConfig, MultiCommCMDConfig):
port: int = field(
"Port to bind to",
default=8080,
)
addr: str = field(
"Address to bind to",
default="127.0.0.1",
)
upload_dir: str = field(
"Directory to store uploaded files in",
default=None,
)
static: str = field(
"Directory to serve static content from",
default=None,
)
js: bool = field(
"Serve JavaScript API file at /api.js",
default=False,
action="store_true",
)
insecure: bool = field(
"Start without TLS encryption",
action="store_true",
default=False,
)
cors_domains: List[str] = field(
"Domains to allow CORS for (see keys in defaults dict for aiohttp_cors.setup)",
default_factory=lambda: [],
)
models: Model = field(
"Models configured on start",
default_factory=lambda: AsyncContextManagerList(),
action=list_action(AsyncContextManagerList),
labeled=True,
)
sources: Sources = field(
"Sources configured on start",
default_factory=lambda: Sources(),
action=list_action(Sources),
labeled=True,
)
async def train(self, sources: Sources):
async for record in sources.records():
self.trained_on[record.key] = record
async def accuracy(self, sources: Sources) -> Accuracy:
accuracy: int = 0
async for record in sources.records():
accuracy += int(record.key)
return Accuracy(accuracy)
class Server(TLSCMD, MultiCommCMD, Routes):
"""
HTTP server providing access to DFFML APIs
"""
# Used for testing
RUN_YIELD_START = False
RUN_YIELD_FINISH = False
INSECURE_NO_TLS = False
arg_port = Arg("-port", help="Port to bind to", type=int, default=8080)
arg_addr = Arg("-addr", help="Address to bind to", default="127.0.0.1")
arg_upload_dir = Arg(
"-upload-dir",
help="Directory to store uploaded files in",
default=None,
)
arg_static = Arg("-static",
help="Directory to serve static content from",
default=None)
arg_js = Arg(
"-js",
help="Serve JavaScript API file at /api.js",
default=False,
action="store_true",
)
arg_insecure = Arg(
"-insecure",
help="Start without TLS encryption",
action="store_true",
default=False,
)
arg_cors_domains = Arg(
"-cors-domains",
help=
"Domains to allow CORS for (see keys in defaults dict for aiohttp_cors.setup)",
nargs="+",
default=[],
)
arg_models = Arg(
"-models",
help="Models configured on start",
nargs="+",
default=AsyncContextManagerList(),
type=Model.load_labeled,
action=list_action(AsyncContextManagerList),
)
arg_sources = Arg(
"-sources",
help="Sources configured on start",
nargs="+",
default=Sources(),
type=BaseSource.load_labeled,
action=list_action(Sources),
)
async def start(self):
if self.insecure:
self.site = web.TCPSite(self.runner,
host=self.addr,
port=self.port)
else:
ssl_context = ssl.create_default_context(
purpose=ssl.Purpose.SERVER_AUTH, cafile=self.cert)
ssl_context.load_cert_chain(self.cert, self.key)
self.site = web.TCPSite(
self.runner,
host=self.addr,
port=self.port,
ssl_context=ssl_context,
)
await self.site.start()
self.port = self.site._server.sockets[0].getsockname()[1]
self.logger.info(f"Serving on {self.addr}:{self.port}")
async def run(self):
"""
Binds to port and starts HTTP server
"""
# Create dictionaries to hold configured sources and models
await self.setup()
await self.start()
# Load
if self.mc_config is not None:
# Restore atomic after config is set, allow setting for now
atomic = self.mc_atomic
self.mc_atomic = False
await self.register_directory(self.mc_config)
self.mc_atomic = atomic
try:
# If we are testing then RUN_YIELD will be an asyncio.Event
if self.RUN_YIELD_START is not False:
await self.RUN_YIELD_START.put(self)
await self.RUN_YIELD_FINISH.wait()
else: # pragma: no cov
# Wait for ctrl-c
while True:
await asyncio.sleep(60)
finally:
await self.app.cleanup()
await self.site.stop()