code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
values |
|---|---|---|---|---|---|---|---|
async def async_set_preset_mode(self, preset_mode: str) -> None:
"""Set the preset mode of the fan."""
_LOGGER.debug("Setting the preset mode to: %s", preset_mode)
await self._try_command(
"Setting preset mode of the miio device failed.",
self._device.set_mode,
... | Set the preset mode of the fan. | async_set_preset_mode | python | syssi/xiaomi_airpurifier | custom_components/xiaomi_miio_airpurifier/fan.py | https://github.com/syssi/xiaomi_airpurifier/blob/master/custom_components/xiaomi_miio_airpurifier/fan.py | Apache-2.0 |
async def async_set_preset_mode(self, preset_mode: str) -> None:
"""Set the preset mode of the fan."""
_LOGGER.debug("Setting the preset mode to: %s", preset_mode)
await self._try_command(
"Setting preset mode of the miio device failed.",
self._device.set_mode,
... | Set the preset mode of the fan. | async_set_preset_mode | python | syssi/xiaomi_airpurifier | custom_components/xiaomi_miio_airpurifier/fan.py | https://github.com/syssi/xiaomi_airpurifier/blob/master/custom_components/xiaomi_miio_airpurifier/fan.py | Apache-2.0 |
async def async_set_preset_mode(self, preset_mode: str) -> None:
"""Set the preset mode of the fan."""
_LOGGER.debug("Setting the preset mode to: %s", preset_mode)
await self._try_command(
"Setting preset mode of the miio device failed.",
self._device.set_mode,
... | Set the preset mode of the fan. | async_set_preset_mode | python | syssi/xiaomi_airpurifier | custom_components/xiaomi_miio_airpurifier/fan.py | https://github.com/syssi/xiaomi_airpurifier/blob/master/custom_components/xiaomi_miio_airpurifier/fan.py | Apache-2.0 |
async def async_set_preset_mode(self, preset_mode: str) -> None:
"""Set the preset mode of the fan."""
_LOGGER.debug("Setting the preset mode to: %s", preset_mode)
await self._try_command(
"Setting preset mode of the miio device failed.",
self._device.set_mode,
... | Set the preset mode of the fan. | async_set_preset_mode | python | syssi/xiaomi_airpurifier | custom_components/xiaomi_miio_airpurifier/fan.py | https://github.com/syssi/xiaomi_airpurifier/blob/master/custom_components/xiaomi_miio_airpurifier/fan.py | Apache-2.0 |
async def async_reset_filter(self):
"""Reset the filter lifetime and usage."""
if self._device_features & FEATURE_RESET_FILTER == 0:
return
await self._try_command(
"Resetting the filter lifetime of the miio device failed.",
self._device.reset_filter,
... | Reset the filter lifetime and usage. | async_reset_filter | python | syssi/xiaomi_airpurifier | custom_components/xiaomi_miio_airpurifier/fan.py | https://github.com/syssi/xiaomi_airpurifier/blob/master/custom_components/xiaomi_miio_airpurifier/fan.py | Apache-2.0 |
async def async_set_preset_mode(self, preset_mode: str) -> None:
"""Set the preset mode of the fan."""
_LOGGER.debug("Setting the preset mode to: %s", preset_mode)
await self._try_command(
"Setting preset mode of the miio device failed.",
self._device.set_mode,
... | Set the preset mode of the fan. | async_set_preset_mode | python | syssi/xiaomi_airpurifier | custom_components/xiaomi_miio_airpurifier/fan.py | https://github.com/syssi/xiaomi_airpurifier/blob/master/custom_components/xiaomi_miio_airpurifier/fan.py | Apache-2.0 |
async def async_reset_filter(self):
"""Reset the filter lifetime and usage."""
if self._device_features & FEATURE_RESET_FILTER == 0:
return
await self._try_command(
"Resetting the upper filter lifetime of the miio device failed.",
self._device.reset_upper_fil... | Reset the filter lifetime and usage. | async_reset_filter | python | syssi/xiaomi_airpurifier | custom_components/xiaomi_miio_airpurifier/fan.py | https://github.com/syssi/xiaomi_airpurifier/blob/master/custom_components/xiaomi_miio_airpurifier/fan.py | Apache-2.0 |
async def async_reset_filter(self):
"""Reset the filter lifetime and usage."""
if self._device_features & FEATURE_RESET_FILTER == 0:
return
await self._try_command(
"Resetting filter lifetime of the miio device failed.",
self._device.reset_filter,
) | Reset the filter lifetime and usage. | async_reset_filter | python | syssi/xiaomi_airpurifier | custom_components/xiaomi_miio_airpurifier/fan.py | https://github.com/syssi/xiaomi_airpurifier/blob/master/custom_components/xiaomi_miio_airpurifier/fan.py | Apache-2.0 |
async def async_set_preset_mode(self, preset_mode: str) -> None:
"""Set the preset mode of the fan."""
_LOGGER.debug("Setting the preset mode to: %s", preset_mode)
if preset_mode == SPEED_OFF:
await self.async_turn_off()
return
if self._natural_mode:
... | Set the preset mode of the fan. | async_set_preset_mode | python | syssi/xiaomi_airpurifier | custom_components/xiaomi_miio_airpurifier/fan.py | https://github.com/syssi/xiaomi_airpurifier/blob/master/custom_components/xiaomi_miio_airpurifier/fan.py | Apache-2.0 |
async def async_set_percentage(self, percentage: int) -> None:
"""Set the speed percentage of the fan."""
_LOGGER.debug("Setting the fan speed percentage to: %s", percentage)
if percentage == 0:
await self.async_turn_off()
return
if self._natural_mode:
... | Set the speed percentage of the fan. | async_set_percentage | python | syssi/xiaomi_airpurifier | custom_components/xiaomi_miio_airpurifier/fan.py | https://github.com/syssi/xiaomi_airpurifier/blob/master/custom_components/xiaomi_miio_airpurifier/fan.py | Apache-2.0 |
async def async_set_direction(self, direction: str) -> None:
"""Set the direction of the fan."""
if direction == "forward":
direction = "right"
if direction == "reverse":
direction = "left"
if self._oscillate:
await self._try_command(
... | Set the direction of the fan. | async_set_direction | python | syssi/xiaomi_airpurifier | custom_components/xiaomi_miio_airpurifier/fan.py | https://github.com/syssi/xiaomi_airpurifier/blob/master/custom_components/xiaomi_miio_airpurifier/fan.py | Apache-2.0 |
async def async_set_delay_off(self, delay_off_countdown: int) -> None:
"""Set scheduled off timer in minutes."""
await self._try_command(
"Setting delay off miio device failed.",
self._device.delay_off,
delay_off_countdown * 60,
) | Set scheduled off timer in minutes. | async_set_delay_off | python | syssi/xiaomi_airpurifier | custom_components/xiaomi_miio_airpurifier/fan.py | https://github.com/syssi/xiaomi_airpurifier/blob/master/custom_components/xiaomi_miio_airpurifier/fan.py | Apache-2.0 |
async def async_set_preset_mode(self, preset_mode: str) -> None:
"""Set the preset mode of the fan."""
_LOGGER.debug("Setting the preset mode to: %s", preset_mode)
if preset_mode == SPEED_OFF:
await self.async_turn_off()
return
await self._try_command(
... | Set the preset mode of the fan. | async_set_preset_mode | python | syssi/xiaomi_airpurifier | custom_components/xiaomi_miio_airpurifier/fan.py | https://github.com/syssi/xiaomi_airpurifier/blob/master/custom_components/xiaomi_miio_airpurifier/fan.py | Apache-2.0 |
async def async_set_percentage(self, percentage: int) -> None:
"""Set the speed percentage of the fan."""
_LOGGER.debug("Setting the fan speed percentage to: %s", percentage)
if percentage == 0:
await self.async_turn_off()
return
await self._try_command(
... | Set the speed percentage of the fan. | async_set_percentage | python | syssi/xiaomi_airpurifier | custom_components/xiaomi_miio_airpurifier/fan.py | https://github.com/syssi/xiaomi_airpurifier/blob/master/custom_components/xiaomi_miio_airpurifier/fan.py | Apache-2.0 |
async def async_set_delay_off(self, delay_off_countdown: int) -> None:
"""Set scheduled off timer in minutes."""
await self._try_command(
"Setting delay off miio device failed.",
self._device.delay_off,
delay_off_countdown,
) | Set scheduled off timer in minutes. | async_set_delay_off | python | syssi/xiaomi_airpurifier | custom_components/xiaomi_miio_airpurifier/fan.py | https://github.com/syssi/xiaomi_airpurifier/blob/master/custom_components/xiaomi_miio_airpurifier/fan.py | Apache-2.0 |
async def async_set_preset_mode(self, preset_mode: str) -> None:
"""Set the preset mode of the fan."""
_LOGGER.debug("Setting the preset mode to: %s", preset_mode)
await self._try_command(
"Setting preset mode of the miio device failed.",
self._device.set_mode,
... | Set the preset mode of the fan. | async_set_preset_mode | python | syssi/xiaomi_airpurifier | custom_components/xiaomi_miio_airpurifier/fan.py | https://github.com/syssi/xiaomi_airpurifier/blob/master/custom_components/xiaomi_miio_airpurifier/fan.py | Apache-2.0 |
async def async_set_percentage(self, percentage: int) -> None:
"""Set the speed percentage of the fan."""
_LOGGER.debug("Setting the fan speed percentage to: %s", percentage)
if percentage == 0:
await self.async_turn_off()
return
await self._try_command(
... | Set the speed percentage of the fan. | async_set_percentage | python | syssi/xiaomi_airpurifier | custom_components/xiaomi_miio_airpurifier/fan.py | https://github.com/syssi/xiaomi_airpurifier/blob/master/custom_components/xiaomi_miio_airpurifier/fan.py | Apache-2.0 |
async def async_set_delay_off(self, delay_off_countdown: int) -> None:
"""Set scheduled off timer in minutes."""
await self._try_command(
"Setting delay off miio device failed.",
self._device.delay_off,
delay_off_countdown,
) | Set scheduled off timer in minutes. | async_set_delay_off | python | syssi/xiaomi_airpurifier | custom_components/xiaomi_miio_airpurifier/fan.py | https://github.com/syssi/xiaomi_airpurifier/blob/master/custom_components/xiaomi_miio_airpurifier/fan.py | Apache-2.0 |
async def async_set_preset_mode(self, preset_mode: str) -> None:
"""Set the preset mode of the fan."""
_LOGGER.debug("Setting the preset mode to: %s", preset_mode)
if not self._state:
await self._try_command(
"Turning the miio device on failed.", self._device.on
... | Set the preset mode of the fan. | async_set_preset_mode | python | syssi/xiaomi_airpurifier | custom_components/xiaomi_miio_airpurifier/fan.py | https://github.com/syssi/xiaomi_airpurifier/blob/master/custom_components/xiaomi_miio_airpurifier/fan.py | Apache-2.0 |
async def async_set_percentage(self, percentage: int) -> None:
"""Set the speed percentage of the fan."""
_LOGGER.debug("Setting the fan speed percentage to: %s", percentage)
if percentage == 0:
await self.async_turn_off()
return
if not self._state:
... | Set the speed percentage of the fan. | async_set_percentage | python | syssi/xiaomi_airpurifier | custom_components/xiaomi_miio_airpurifier/fan.py | https://github.com/syssi/xiaomi_airpurifier/blob/master/custom_components/xiaomi_miio_airpurifier/fan.py | Apache-2.0 |
async def async_set_delay_off(self, delay_off_countdown: int) -> None:
"""Set scheduled off timer in minutes."""
await self._try_command(
"Setting delay off miio device failed.",
self._device.delay_off,
delay_off_countdown,
) | Set scheduled off timer in minutes. | async_set_delay_off | python | syssi/xiaomi_airpurifier | custom_components/xiaomi_miio_airpurifier/fan.py | https://github.com/syssi/xiaomi_airpurifier/blob/master/custom_components/xiaomi_miio_airpurifier/fan.py | Apache-2.0 |
async def async_set_preset_mode(self, preset_mode: str) -> None:
"""Set the preset mode of the fan."""
_LOGGER.debug("Setting the preset mode to: %s", preset_mode)
_LOGGER.debug(
"Calling set_mode_and_speed with parameters: %s",
self._preset_modes_to_mode_speed[preset_mod... | Set the preset mode of the fan. | async_set_preset_mode | python | syssi/xiaomi_airpurifier | custom_components/xiaomi_miio_airpurifier/fan.py | https://github.com/syssi/xiaomi_airpurifier/blob/master/custom_components/xiaomi_miio_airpurifier/fan.py | Apache-2.0 |
def get_from_config():
"""Get benchmarks configuration from the config.json file"""
current_path = Path(__file__).resolve().parent
config_path = current_path / "config.json"
with open(config_path, "r") as config_file:
config_file = "".join(line for line in config_file if line and "//" not in li... | Get benchmarks configuration from the config.json file | get_from_config | python | scikit-learn/scikit-learn | asv_benchmarks/benchmarks/common.py | https://github.com/scikit-learn/scikit-learn/blob/master/asv_benchmarks/benchmarks/common.py | BSD-3-Clause |
def get_estimator_path(benchmark, directory, params, save=False):
"""Get path of pickled fitted estimator"""
path = Path(__file__).resolve().parent / "cache"
path = (path / "estimators" / directory) if save else (path / "tmp")
filename = (
benchmark.__class__.__name__
+ "_estimator_"
... | Get path of pickled fitted estimator | get_estimator_path | python | scikit-learn/scikit-learn | asv_benchmarks/benchmarks/common.py | https://github.com/scikit-learn/scikit-learn/blob/master/asv_benchmarks/benchmarks/common.py | BSD-3-Clause |
def make_data(self, params):
"""Return the dataset for a combination of parameters"""
# The datasets are cached using joblib.Memory so it's fast and can be
# called for each repeat
pass | Return the dataset for a combination of parameters | make_data | python | scikit-learn/scikit-learn | asv_benchmarks/benchmarks/common.py | https://github.com/scikit-learn/scikit-learn/blob/master/asv_benchmarks/benchmarks/common.py | BSD-3-Clause |
def setup_cache(self):
"""Pickle a fitted estimator for all combinations of parameters"""
# This is run once per benchmark class.
clear_tmp()
param_grid = list(itertools.product(*self.params))
for params in param_grid:
if self.skip(params):
continue... | Pickle a fitted estimator for all combinations of parameters | setup_cache | python | scikit-learn/scikit-learn | asv_benchmarks/benchmarks/common.py | https://github.com/scikit-learn/scikit-learn/blob/master/asv_benchmarks/benchmarks/common.py | BSD-3-Clause |
def setup(self, *params):
"""Generate dataset and load the fitted estimator"""
# This is run once per combination of parameters and per repeat so we
# need to avoid doing expensive operations there.
if self.skip(params):
raise NotImplementedError
self.X, self.X_val,... | Generate dataset and load the fitted estimator | setup | python | scikit-learn/scikit-learn | asv_benchmarks/benchmarks/common.py | https://github.com/scikit-learn/scikit-learn/blob/master/asv_benchmarks/benchmarks/common.py | BSD-3-Clause |
def load_data(dtype=np.float32, order="C", random_state=13):
"""Load the data, then cache and memmap the train/test split"""
######################################################################
# Load dataset
print("Loading dataset...")
data = fetch_covtype(
download_if_missing=True, shuff... | Load the data, then cache and memmap the train/test split | load_data | python | scikit-learn/scikit-learn | benchmarks/bench_covertype.py | https://github.com/scikit-learn/scikit-learn/blob/master/benchmarks/bench_covertype.py | BSD-3-Clause |
def print_outlier_ratio(y):
"""
Helper function to show the distinct value count of element in the target.
Useful indicator for the datasets used in bench_isolation_forest.py.
"""
uniq, cnt = np.unique(y, return_counts=True)
print("----- Target count values: ")
for u, c in zip(uniq, cnt):
... |
Helper function to show the distinct value count of element in the target.
Useful indicator for the datasets used in bench_isolation_forest.py.
| print_outlier_ratio | python | scikit-learn/scikit-learn | benchmarks/bench_isolation_forest.py | https://github.com/scikit-learn/scikit-learn/blob/master/benchmarks/bench_isolation_forest.py | BSD-3-Clause |
def get_data(
n_samples_train, n_samples_test, n_features, contamination=0.1, random_state=0
):
"""Function based on code from: https://scikit-learn.org/stable/
auto_examples/ensemble/plot_isolation_forest.html#sphx-glr-auto-
examples-ensemble-plot-isolation-forest-py
"""
rng = np.random.RandomS... | Function based on code from: https://scikit-learn.org/stable/
auto_examples/ensemble/plot_isolation_forest.html#sphx-glr-auto-
examples-ensemble-plot-isolation-forest-py
| get_data | python | scikit-learn/scikit-learn | benchmarks/bench_isolation_forest_predict.py | https://github.com/scikit-learn/scikit-learn/blob/master/benchmarks/bench_isolation_forest_predict.py | BSD-3-Clause |
def bench_isotonic_regression(Y):
"""
Runs a single iteration of isotonic regression on the input data,
and reports the total time taken (in seconds).
"""
gc.collect()
tstart = default_timer()
isotonic_regression(Y)
return default_timer() - tstart |
Runs a single iteration of isotonic regression on the input data,
and reports the total time taken (in seconds).
| bench_isotonic_regression | python | scikit-learn/scikit-learn | benchmarks/bench_isotonic.py | https://github.com/scikit-learn/scikit-learn/blob/master/benchmarks/bench_isotonic.py | BSD-3-Clause |
def load_data(dtype=np.float32, order="F"):
"""Load the data, then cache and memmap the train/test split"""
######################################################################
# Load dataset
print("Loading dataset...")
data = fetch_openml("mnist_784", as_frame=True)
X = check_array(data["data... | Load the data, then cache and memmap the train/test split | load_data | python | scikit-learn/scikit-learn | benchmarks/bench_mnist.py | https://github.com/scikit-learn/scikit-learn/blob/master/benchmarks/bench_mnist.py | BSD-3-Clause |
def benchmark(
metrics=tuple(v for k, v in sorted(METRICS.items())),
formats=tuple(v for k, v in sorted(FORMATS.items())),
samples=1000,
classes=4,
density=0.2,
n_times=5,
):
"""Times metric calculations for a number of inputs
Parameters
----------
metrics : array-like of callab... | Times metric calculations for a number of inputs
Parameters
----------
metrics : array-like of callables (1d or 0d)
The metric functions to time.
formats : array-like of callables (1d or 0d)
These may transform a dense indicator matrix into multilabel
representation.
sampl... | benchmark | python | scikit-learn/scikit-learn | benchmarks/bench_multilabel_metrics.py | https://github.com/scikit-learn/scikit-learn/blob/master/benchmarks/bench_multilabel_metrics.py | BSD-3-Clause |
def _tabulate(results, metrics, formats):
"""Prints results by metric and format
Uses the last ([-1]) value of other fields
"""
column_width = max(max(len(k) for k in formats) + 1, 8)
first_width = max(len(k) for k in metrics)
head_fmt = "{:<{fw}s}" + "{:>{cw}s}" * len(formats)
row_fmt = "{... | Prints results by metric and format
Uses the last ([-1]) value of other fields
| _tabulate | python | scikit-learn/scikit-learn | benchmarks/bench_multilabel_metrics.py | https://github.com/scikit-learn/scikit-learn/blob/master/benchmarks/bench_multilabel_metrics.py | BSD-3-Clause |
def _plot(
results,
metrics,
formats,
title,
x_ticks,
x_label,
format_markers=("x", "|", "o", "+"),
metric_colors=("c", "m", "y", "k", "g", "r", "b"),
):
"""
Plot the results by metric, format and some other variable given by
x_label
"""
fig = plt.figure("scikit-learn... |
Plot the results by metric, format and some other variable given by
x_label
| _plot | python | scikit-learn/scikit-learn | benchmarks/bench_multilabel_metrics.py | https://github.com/scikit-learn/scikit-learn/blob/master/benchmarks/bench_multilabel_metrics.py | BSD-3-Clause |
def print_outlier_ratio(y):
"""
Helper function to show the distinct value count of element in the target.
Useful indicator for the datasets used in bench_isolation_forest.py.
"""
uniq, cnt = np.unique(y, return_counts=True)
print("----- Target count values: ")
for u, c in zip(uniq, cnt):
... |
Helper function to show the distinct value count of element in the target.
Useful indicator for the datasets used in bench_isolation_forest.py.
| print_outlier_ratio | python | scikit-learn/scikit-learn | benchmarks/bench_online_ocsvm.py | https://github.com/scikit-learn/scikit-learn/blob/master/benchmarks/bench_online_ocsvm.py | BSD-3-Clause |
def autolabel_auc(rects, ax):
"""Attach a text label above each bar displaying its height."""
for rect in rects:
height = rect.get_height()
ax.text(
rect.get_x() + rect.get_width() / 2.0,
1.05 * height,
"%.3f" % height,
ha="center",
va=... | Attach a text label above each bar displaying its height. | autolabel_auc | python | scikit-learn/scikit-learn | benchmarks/bench_online_ocsvm.py | https://github.com/scikit-learn/scikit-learn/blob/master/benchmarks/bench_online_ocsvm.py | BSD-3-Clause |
def autolabel_time(rects, ax):
"""Attach a text label above each bar displaying its height."""
for rect in rects:
height = rect.get_height()
ax.text(
rect.get_x() + rect.get_width() / 2.0,
1.05 * height,
"%.1f" % height,
ha="center",
va... | Attach a text label above each bar displaying its height. | autolabel_time | python | scikit-learn/scikit-learn | benchmarks/bench_online_ocsvm.py | https://github.com/scikit-learn/scikit-learn/blob/master/benchmarks/bench_online_ocsvm.py | BSD-3-Clause |
def _nls_subproblem(
X, W, H, tol, max_iter, alpha=0.0, l1_ratio=0.0, sigma=0.01, beta=0.1
):
"""Non-negative least square solver
Solves a non-negative least squares subproblem using the projected
gradient descent algorithm.
Parameters
----------
X : array-like, shape (n_samples, n_features)... | Non-negative least square solver
Solves a non-negative least squares subproblem using the projected
gradient descent algorithm.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Constant matrix.
W : array-like, shape (n_samples, n_components)
Constant matrix.
... | _nls_subproblem | python | scikit-learn/scikit-learn | benchmarks/bench_plot_nmf.py | https://github.com/scikit-learn/scikit-learn/blob/master/benchmarks/bench_plot_nmf.py | BSD-3-Clause |
def norm_diff(A, norm=2, msg=True, random_state=None):
"""
Compute the norm diff with the original matrix, when randomized
SVD is called with *params.
norm: 2 => spectral; 'fro' => Frobenius
"""
if msg:
print("... computing %s norm ..." % norm)
if norm == 2:
# s = sp.linalg... |
Compute the norm diff with the original matrix, when randomized
SVD is called with *params.
norm: 2 => spectral; 'fro' => Frobenius
| norm_diff | python | scikit-learn/scikit-learn | benchmarks/bench_plot_randomized_svd.py | https://github.com/scikit-learn/scikit-learn/blob/master/benchmarks/bench_plot_randomized_svd.py | BSD-3-Clause |
def bench_scikit_tree_classifier(X, Y):
"""Benchmark with scikit-learn decision tree classifier"""
from sklearn.tree import DecisionTreeClassifier
gc.collect()
# start time
tstart = datetime.now()
clf = DecisionTreeClassifier()
clf.fit(X, Y).predict(X)
delta = datetime.now() - tstart
... | Benchmark with scikit-learn decision tree classifier | bench_scikit_tree_classifier | python | scikit-learn/scikit-learn | benchmarks/bench_tree.py | https://github.com/scikit-learn/scikit-learn/blob/master/benchmarks/bench_tree.py | BSD-3-Clause |
def bench_scikit_tree_regressor(X, Y):
"""Benchmark with scikit-learn decision tree regressor"""
from sklearn.tree import DecisionTreeRegressor
gc.collect()
# start time
tstart = datetime.now()
clf = DecisionTreeRegressor()
clf.fit(X, Y).predict(X)
delta = datetime.now() - tstart
... | Benchmark with scikit-learn decision tree regressor | bench_scikit_tree_regressor | python | scikit-learn/scikit-learn | benchmarks/bench_tree.py | https://github.com/scikit-learn/scikit-learn/blob/master/benchmarks/bench_tree.py | BSD-3-Clause |
def load_data(dtype=np.float32, order="C", shuffle=True, seed=0):
"""Load the data, then cache and memmap the train/test split"""
print("Loading dataset...")
data = fetch_openml("mnist_784", as_frame=True)
X = check_array(data["data"], dtype=dtype, order=order)
y = data["target"]
if shuffle:
... | Load the data, then cache and memmap the train/test split | load_data | python | scikit-learn/scikit-learn | benchmarks/bench_tsne_mnist.py | https://github.com/scikit-learn/scikit-learn/blob/master/benchmarks/bench_tsne_mnist.py | BSD-3-Clause |
def nn_accuracy(X, X_embedded, k=1):
"""Accuracy of the first nearest neighbor"""
knn = NearestNeighbors(n_neighbors=1, n_jobs=-1)
_, neighbors_X = knn.fit(X).kneighbors()
_, neighbors_X_embedded = knn.fit(X_embedded).kneighbors()
return np.mean(neighbors_X == neighbors_X_embedded) | Accuracy of the first nearest neighbor | nn_accuracy | python | scikit-learn/scikit-learn | benchmarks/bench_tsne_mnist.py | https://github.com/scikit-learn/scikit-learn/blob/master/benchmarks/bench_tsne_mnist.py | BSD-3-Clause |
def bhtsne(X):
"""Wrapper for the reference lvdmaaten/bhtsne implementation."""
# PCA preprocessing is done elsewhere in the benchmark script
n_iter = -1 # TODO find a way to report the number of iterations
return (
run_bh_tsne(
X,
... | Wrapper for the reference lvdmaaten/bhtsne implementation. | bhtsne | python | scikit-learn/scikit-learn | benchmarks/bench_tsne_mnist.py | https://github.com/scikit-learn/scikit-learn/blob/master/benchmarks/bench_tsne_mnist.py | BSD-3-Clause |
def has_openmp_flags(target):
"""Return whether target sources use OpenMP flags.
Make sure that both compiler and linker source use OpenMP.
Look at `get_meson_info` docstring to see what `target` looks like.
"""
target_sources = target["target_sources"]
target_use_openmp_flags = any(
h... | Return whether target sources use OpenMP flags.
Make sure that both compiler and linker source use OpenMP.
Look at `get_meson_info` docstring to see what `target` looks like.
| has_openmp_flags | python | scikit-learn/scikit-learn | build_tools/check-meson-openmp-dependencies.py | https://github.com/scikit-learn/scikit-learn/blob/master/build_tools/check-meson-openmp-dependencies.py | BSD-3-Clause |
def get_canonical_name_meson(target, build_path):
"""Return a name based on generated shared library.
The goal is to return a name that can be easily matched with the output
from `git_grep_info`.
Look at `get_meson_info` docstring to see what `target` looks like.
"""
# Expect a list with one e... | Return a name based on generated shared library.
The goal is to return a name that can be easily matched with the output
from `git_grep_info`.
Look at `get_meson_info` docstring to see what `target` looks like.
| get_canonical_name_meson | python | scikit-learn/scikit-learn | build_tools/check-meson-openmp-dependencies.py | https://github.com/scikit-learn/scikit-learn/blob/master/build_tools/check-meson-openmp-dependencies.py | BSD-3-Clause |
def get_meson_info():
"""Return names of extension that use OpenMP based on meson introspect output.
The meson introspect json info is a list of targets where a target is a dict
that looks like this (parts not used in this script are not shown for simplicity):
{
'name': '_k_means_elkan.cpython-31... | Return names of extension that use OpenMP based on meson introspect output.
The meson introspect json info is a list of targets where a target is a dict
that looks like this (parts not used in this script are not shown for simplicity):
{
'name': '_k_means_elkan.cpython-312-x86_64-linux-gnu',
'f... | get_meson_info | python | scikit-learn/scikit-learn | build_tools/check-meson-openmp-dependencies.py | https://github.com/scikit-learn/scikit-learn/blob/master/build_tools/check-meson-openmp-dependencies.py | BSD-3-Clause |
def get_git_grep_info():
"""Return names of extensions that use OpenMP based on git grep regex."""
git_grep_filenames = subprocess.check_output(
["git", "grep", "-lP", "cython.*parallel|_openmp_helpers"], text=True
).splitlines()
git_grep_filenames = [f for f in git_grep_filenames if ".pyx" in f... | Return names of extensions that use OpenMP based on git grep regex. | get_git_grep_info | python | scikit-learn/scikit-learn | build_tools/check-meson-openmp-dependencies.py | https://github.com/scikit-learn/scikit-learn/blob/master/build_tools/check-meson-openmp-dependencies.py | BSD-3-Clause |
def get_contributors():
"""Get the list of contributor profiles. Require admin rights."""
# get core devs and contributor experience team
core_devs = []
documentation_team = []
contributor_experience_team = []
comm_team = []
core_devs_slug = "core-devs"
contributor_experience_team_slug =... | Get the list of contributor profiles. Require admin rights. | get_contributors | python | scikit-learn/scikit-learn | build_tools/generate_authors_table.py | https://github.com/scikit-learn/scikit-learn/blob/master/build_tools/generate_authors_table.py | BSD-3-Clause |
def get_profile(login):
"""Get the GitHub profile from login"""
print("get profile for %s" % (login,))
try:
profile = get("https://api.github.com/users/%s" % login).json()
except requests.exceptions.HTTPError:
return dict(name=login, avatar_url=LOGO_URL, html_url="")
if profile["nam... | Get the GitHub profile from login | get_profile | python | scikit-learn/scikit-learn | build_tools/generate_authors_table.py | https://github.com/scikit-learn/scikit-learn/blob/master/build_tools/generate_authors_table.py | BSD-3-Clause |
def key(profile):
"""Get a sorting key based on the lower case last name, then firstname"""
components = profile["name"].lower().split(" ")
return " ".join([components[-1]] + components[:-1]) | Get a sorting key based on the lower case last name, then firstname | key | python | scikit-learn/scikit-learn | build_tools/generate_authors_table.py | https://github.com/scikit-learn/scikit-learn/blob/master/build_tools/generate_authors_table.py | BSD-3-Clause |
def get_versions(versions_file):
"""Get the versions of the packages used in the linter job.
Parameters
----------
versions_file : str
The path to the file that contains the versions of the packages.
Returns
-------
versions : dict
A dictionary with the versions of the pack... | Get the versions of the packages used in the linter job.
Parameters
----------
versions_file : str
The path to the file that contains the versions of the packages.
Returns
-------
versions : dict
A dictionary with the versions of the packages.
| get_versions | python | scikit-learn/scikit-learn | build_tools/get_comment.py | https://github.com/scikit-learn/scikit-learn/blob/master/build_tools/get_comment.py | BSD-3-Clause |
def get_step_message(log, start, end, title, message, details):
"""Get the message for a specific test.
Parameters
----------
log : str
The log of the linting job.
start : str
The string that marks the start of the test.
end : str
The string that marks the end of the t... | Get the message for a specific test.
Parameters
----------
log : str
The log of the linting job.
start : str
The string that marks the start of the test.
end : str
The string that marks the end of the test.
title : str
The title for this section.
message ... | get_step_message | python | scikit-learn/scikit-learn | build_tools/get_comment.py | https://github.com/scikit-learn/scikit-learn/blob/master/build_tools/get_comment.py | BSD-3-Clause |
def get_headers(token):
"""Get the headers for the GitHub API."""
return {
"Accept": "application/vnd.github+json",
"Authorization": f"Bearer {token}",
"X-GitHub-Api-Version": "2022-11-28",
} | Get the headers for the GitHub API. | get_headers | python | scikit-learn/scikit-learn | build_tools/get_comment.py | https://github.com/scikit-learn/scikit-learn/blob/master/build_tools/get_comment.py | BSD-3-Clause |
def create_or_update_comment(comment, message, repo, pr_number, token):
"""Create a new comment or update existing one."""
# repo is in the form of "org/repo"
if comment is not None:
print("updating existing comment")
# API doc: https://docs.github.com/en/rest/issues/comments?apiVersion=2022... | Create a new comment or update existing one. | create_or_update_comment | python | scikit-learn/scikit-learn | build_tools/get_comment.py | https://github.com/scikit-learn/scikit-learn/blob/master/build_tools/get_comment.py | BSD-3-Clause |
def make_distributor_init_64_bits(
distributor_init,
vcomp140_dll_filename,
msvcp140_dll_filename,
):
"""Create a _distributor_init.py file for 64-bit architectures.
This file is imported first when importing the sklearn package
so as to pre-load the vendored vcomp140.dll and msvcp140.dll.
... | Create a _distributor_init.py file for 64-bit architectures.
This file is imported first when importing the sklearn package
so as to pre-load the vendored vcomp140.dll and msvcp140.dll.
| make_distributor_init_64_bits | python | scikit-learn/scikit-learn | build_tools/github/vendor.py | https://github.com/scikit-learn/scikit-learn/blob/master/build_tools/github/vendor.py | BSD-3-Clause |
def _get_guide(*refs, is_developer=False):
"""Get the rst to refer to user/developer guide.
`refs` is several references that can be used in the :ref:`...` directive.
"""
if len(refs) == 1:
ref_desc = f":ref:`{refs[0]}` section"
elif len(refs) == 2:
ref_desc = f":ref:`{refs[0]}` and... | Get the rst to refer to user/developer guide.
`refs` is several references that can be used in the :ref:`...` directive.
| _get_guide | python | scikit-learn/scikit-learn | doc/api_reference.py | https://github.com/scikit-learn/scikit-learn/blob/master/doc/api_reference.py | BSD-3-Clause |
def _get_submodule(module_name, submodule_name):
"""Get the submodule docstring and automatically add the hook.
`module_name` is e.g. `sklearn.feature_extraction`, and `submodule_name` is e.g.
`image`, so we get the docstring and hook for `sklearn.feature_extraction.image`
submodule. `module_name` is u... | Get the submodule docstring and automatically add the hook.
`module_name` is e.g. `sklearn.feature_extraction`, and `submodule_name` is e.g.
`image`, so we get the docstring and hook for `sklearn.feature_extraction.image`
submodule. `module_name` is used to reset the current module because autosummary
... | _get_submodule | python | scikit-learn/scikit-learn | doc/api_reference.py | https://github.com/scikit-learn/scikit-learn/blob/master/doc/api_reference.py | BSD-3-Clause |
def add_js_css_files(app, pagename, templatename, context, doctree):
"""Load additional JS and CSS files only for certain pages.
Note that `html_js_files` and `html_css_files` are included in all pages and
should be used for the ones that are used by multiple pages. All page-specific
JS and CSS files s... | Load additional JS and CSS files only for certain pages.
Note that `html_js_files` and `html_css_files` are included in all pages and
should be used for the ones that are used by multiple pages. All page-specific
JS and CSS files should be added here instead.
| add_js_css_files | python | scikit-learn/scikit-learn | doc/conf.py | https://github.com/scikit-learn/scikit-learn/blob/master/doc/conf.py | BSD-3-Clause |
def make_carousel_thumbs(app, exception):
"""produces the final resized carousel images"""
if exception is not None:
return
print("Preparing carousel images")
image_dir = os.path.join(app.builder.outdir, "_images")
for glr_plot, max_width in carousel_thumbs.items():
image = os.path.... | produces the final resized carousel images | make_carousel_thumbs | python | scikit-learn/scikit-learn | doc/conf.py | https://github.com/scikit-learn/scikit-learn/blob/master/doc/conf.py | BSD-3-Clause |
def skip_properties(app, what, name, obj, skip, options):
"""Skip properties that are fitted attributes"""
if isinstance(obj, property):
if name.endswith("_") and not name.startswith("_"):
return True
return skip | Skip properties that are fitted attributes | skip_properties | python | scikit-learn/scikit-learn | doc/conf.py | https://github.com/scikit-learn/scikit-learn/blob/master/doc/conf.py | BSD-3-Clause |
def infer_next_release_versions():
"""Infer the most likely next release versions to make."""
all_version_full = {"rc": "0.99.0rc1", "final": "0.99.0", "bf": "0.98.1"}
all_version_short = {"rc": "0.99", "final": "0.99", "bf": "0.98"}
all_previous_tag = {"rc": "unused", "final": "0.98.33", "bf": "0.97.22... | Infer the most likely next release versions to make. | infer_next_release_versions | python | scikit-learn/scikit-learn | doc/conf.py | https://github.com/scikit-learn/scikit-learn/blob/master/doc/conf.py | BSD-3-Clause |
def pytest_collection_modifyitems(config, items):
"""Called after collect is completed.
Parameters
----------
config : pytest config
items : list of collected items
"""
skip_doctests = False
if np_base_version < parse_version("2"):
# TODO: configure numpy to output scalar arrays... | Called after collect is completed.
Parameters
----------
config : pytest config
items : list of collected items
| pytest_collection_modifyitems | python | scikit-learn/scikit-learn | doc/conftest.py | https://github.com/scikit-learn/scikit-learn/blob/master/doc/conftest.py | BSD-3-Clause |
def add_content(self, more_content):
"""Override default behavior to add only the first line of the docstring.
Modified based on the part of processing docstrings in the original
implementation of this method.
https://github.com/sphinx-doc/sphinx/blob/faa33a53a389f6f8bc1f6ae97d6015fa92... | Override default behavior to add only the first line of the docstring.
Modified based on the part of processing docstrings in the original
implementation of this method.
https://github.com/sphinx-doc/sphinx/blob/faa33a53a389f6f8bc1f6ae97d6015fa92393c4a/sphinx/ext/autodoc/__init__.py#L609-L622
... | add_content | python | scikit-learn/scikit-learn | doc/sphinxext/autoshortsummary.py | https://github.com/scikit-learn/scikit-learn/blob/master/doc/sphinxext/autoshortsummary.py | BSD-3-Clause |
def _linkcode_resolve(domain, info, package, url_fmt, revision):
"""Determine a link to online source for a class/method/function
This is called by sphinx.ext.linkcode
An example with a long-untouched module that everyone has
>>> _linkcode_resolve('py', {'module': 'tty',
... ... | Determine a link to online source for a class/method/function
This is called by sphinx.ext.linkcode
An example with a long-untouched module that everyone has
>>> _linkcode_resolve('py', {'module': 'tty',
... 'fullname': 'setraw'},
... package='tty',
.... | _linkcode_resolve | python | scikit-learn/scikit-learn | doc/sphinxext/github_link.py | https://github.com/scikit-learn/scikit-learn/blob/master/doc/sphinxext/github_link.py | BSD-3-Clause |
def override_pst_pagetoc(app, pagename, templatename, context, doctree):
"""Overrides the `generate_toc_html` function of pydata-sphinx-theme for API."""
@cache
def generate_api_toc_html(kind="html"):
"""Generate the in-page toc for an API page.
This relies on the `generate_toc_html` funct... | Overrides the `generate_toc_html` function of pydata-sphinx-theme for API. | override_pst_pagetoc | python | scikit-learn/scikit-learn | doc/sphinxext/override_pst_pagetoc.py | https://github.com/scikit-learn/scikit-learn/blob/master/doc/sphinxext/override_pst_pagetoc.py | BSD-3-Clause |
def generate_api_toc_html(kind="html"):
"""Generate the in-page toc for an API page.
This relies on the `generate_toc_html` function added by pydata-sphinx-theme
into the context. We save the original function into `pst_generate_toc_html`
and override `generate_toc_html` with this funct... | Generate the in-page toc for an API page.
This relies on the `generate_toc_html` function added by pydata-sphinx-theme
into the context. We save the original function into `pst_generate_toc_html`
and override `generate_toc_html` with this function for generated API pages.
The pagetoc o... | generate_api_toc_html | python | scikit-learn/scikit-learn | doc/sphinxext/override_pst_pagetoc.py | https://github.com/scikit-learn/scikit-learn/blob/master/doc/sphinxext/override_pst_pagetoc.py | BSD-3-Clause |
def user_role(name, rawtext, text, lineno, inliner, options=None, content=None):
"""Sphinx role for linking to a user profile. Defaults to linking to
Github profiles, but the profile URIS can be configured via the
``issues_user_uri`` config value.
Examples: ::
:user:`sloria`
Anchor text also... | Sphinx role for linking to a user profile. Defaults to linking to
Github profiles, but the profile URIS can be configured via the
``issues_user_uri`` config value.
Examples: ::
:user:`sloria`
Anchor text also works: ::
:user:`Steven Loria <sloria>`
| user_role | python | scikit-learn/scikit-learn | doc/sphinxext/sphinx_issues.py | https://github.com/scikit-learn/scikit-learn/blob/master/doc/sphinxext/sphinx_issues.py | BSD-3-Clause |
def plot_digits(X, title):
"""Small helper function to plot 100 digits."""
fig, axs = plt.subplots(nrows=10, ncols=10, figsize=(8, 8))
for img, ax in zip(X, axs.ravel()):
ax.imshow(img.reshape((16, 16)), cmap="Greys")
ax.axis("off")
fig.suptitle(title, fontsize=24) | Small helper function to plot 100 digits. | plot_digits | python | scikit-learn/scikit-learn | examples/applications/plot_digits_denoising.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/applications/plot_digits_denoising.py | BSD-3-Clause |
def plot_gallery(images, titles, h, w, n_row=3, n_col=4):
"""Helper function to plot a gallery of portraits"""
plt.figure(figsize=(1.8 * n_col, 2.4 * n_row))
plt.subplots_adjust(bottom=0, left=0.01, right=0.99, top=0.90, hspace=0.35)
for i in range(n_row * n_col):
plt.subplot(n_row, n_col, i + 1... | Helper function to plot a gallery of portraits | plot_gallery | python | scikit-learn/scikit-learn | examples/applications/plot_face_recognition.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/applications/plot_face_recognition.py | BSD-3-Clause |
def benchmark_influence(conf):
"""
Benchmark influence of `changing_param` on both MSE and latency.
"""
prediction_times = []
prediction_powers = []
complexities = []
for param_value in conf["changing_param_values"]:
conf["tuned_params"][conf["changing_param"]] = param_value
... |
Benchmark influence of `changing_param` on both MSE and latency.
| benchmark_influence | python | scikit-learn/scikit-learn | examples/applications/plot_model_complexity_influence.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/applications/plot_model_complexity_influence.py | BSD-3-Clause |
def plot_influence(conf, mse_values, prediction_times, complexities):
"""
Plot influence of model complexity on both accuracy and latency.
"""
fig = plt.figure()
fig.subplots_adjust(right=0.75)
# first axes (prediction error)
ax1 = fig.add_subplot(111)
line1 = ax1.plot(complexities, ms... |
Plot influence of model complexity on both accuracy and latency.
| plot_influence | python | scikit-learn/scikit-learn | examples/applications/plot_model_complexity_influence.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/applications/plot_model_complexity_influence.py | BSD-3-Clause |
def stream_reuters_documents(data_path=None):
"""Iterate over documents of the Reuters dataset.
The Reuters archive will automatically be downloaded and uncompressed if
the `data_path` directory does not exist.
Documents are represented as dictionaries with 'body' (str),
'title' (str), 'topics' (l... | Iterate over documents of the Reuters dataset.
The Reuters archive will automatically be downloaded and uncompressed if
the `data_path` directory does not exist.
Documents are represented as dictionaries with 'body' (str),
'title' (str), 'topics' (list(str)) keys.
| stream_reuters_documents | python | scikit-learn/scikit-learn | examples/applications/plot_out_of_core_classification.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/applications/plot_out_of_core_classification.py | BSD-3-Clause |
def get_minibatch(doc_iter, size, pos_class=positive_class):
"""Extract a minibatch of examples, return a tuple X_text, y.
Note: size is before excluding invalid docs with no topics assigned.
"""
data = [
("{title}\n\n{body}".format(**doc), pos_class in doc["topics"])
for doc in iterto... | Extract a minibatch of examples, return a tuple X_text, y.
Note: size is before excluding invalid docs with no topics assigned.
| get_minibatch | python | scikit-learn/scikit-learn | examples/applications/plot_out_of_core_classification.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/applications/plot_out_of_core_classification.py | BSD-3-Clause |
def autolabel(rectangles):
"""attach some text vi autolabel on rectangles."""
for rect in rectangles:
height = rect.get_height()
ax.text(
rect.get_x() + rect.get_width() / 2.0,
1.05 * height,
"%.4f" % height,
ha="center",
va="bottom",
... | attach some text vi autolabel on rectangles. | autolabel | python | scikit-learn/scikit-learn | examples/applications/plot_out_of_core_classification.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/applications/plot_out_of_core_classification.py | BSD-3-Clause |
def atomic_benchmark_estimator(estimator, X_test, verbose=False):
"""Measure runtime prediction of each instance."""
n_instances = X_test.shape[0]
runtimes = np.zeros(n_instances, dtype=float)
for i in range(n_instances):
instance = X_test[[i], :]
start = time.time()
estimator.pr... | Measure runtime prediction of each instance. | atomic_benchmark_estimator | python | scikit-learn/scikit-learn | examples/applications/plot_prediction_latency.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/applications/plot_prediction_latency.py | BSD-3-Clause |
def bulk_benchmark_estimator(estimator, X_test, n_bulk_repeats, verbose):
"""Measure runtime prediction of the whole input."""
n_instances = X_test.shape[0]
runtimes = np.zeros(n_bulk_repeats, dtype=float)
for i in range(n_bulk_repeats):
start = time.time()
estimator.predict(X_test)
... | Measure runtime prediction of the whole input. | bulk_benchmark_estimator | python | scikit-learn/scikit-learn | examples/applications/plot_prediction_latency.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/applications/plot_prediction_latency.py | BSD-3-Clause |
def benchmark_estimator(estimator, X_test, n_bulk_repeats=30, verbose=False):
"""
Measure runtimes of prediction in both atomic and bulk mode.
Parameters
----------
estimator : already trained estimator supporting `predict()`
X_test : test input
n_bulk_repeats : how many times to repeat whe... |
Measure runtimes of prediction in both atomic and bulk mode.
Parameters
----------
estimator : already trained estimator supporting `predict()`
X_test : test input
n_bulk_repeats : how many times to repeat when evaluating bulk mode
Returns
-------
atomic_runtimes, bulk_runtimes : ... | benchmark_estimator | python | scikit-learn/scikit-learn | examples/applications/plot_prediction_latency.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/applications/plot_prediction_latency.py | BSD-3-Clause |
def generate_dataset(n_train, n_test, n_features, noise=0.1, verbose=False):
"""Generate a regression dataset with the given parameters."""
if verbose:
print("generating dataset...")
X, y, coef = make_regression(
n_samples=n_train + n_test, n_features=n_features, noise=noise, coef=True
... | Generate a regression dataset with the given parameters. | generate_dataset | python | scikit-learn/scikit-learn | examples/applications/plot_prediction_latency.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/applications/plot_prediction_latency.py | BSD-3-Clause |
def boxplot_runtimes(runtimes, pred_type, configuration):
"""
Plot a new `Figure` with boxplots of prediction runtimes.
Parameters
----------
runtimes : list of `np.array` of latencies in micro-seconds
cls_names : list of estimator class names that generated the runtimes
pred_type : 'bulk' ... |
Plot a new `Figure` with boxplots of prediction runtimes.
Parameters
----------
runtimes : list of `np.array` of latencies in micro-seconds
cls_names : list of estimator class names that generated the runtimes
pred_type : 'bulk' or 'atomic'
| boxplot_runtimes | python | scikit-learn/scikit-learn | examples/applications/plot_prediction_latency.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/applications/plot_prediction_latency.py | BSD-3-Clause |
def n_feature_influence(estimators, n_train, n_test, n_features, percentile):
"""
Estimate influence of the number of features on prediction time.
Parameters
----------
estimators : dict of (name (str), estimator) to benchmark
n_train : nber of training instances (int)
n_test : nber of tes... |
Estimate influence of the number of features on prediction time.
Parameters
----------
estimators : dict of (name (str), estimator) to benchmark
n_train : nber of training instances (int)
n_test : nber of testing instances (int)
n_features : list of feature-space dimensionality to test (i... | n_feature_influence | python | scikit-learn/scikit-learn | examples/applications/plot_prediction_latency.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/applications/plot_prediction_latency.py | BSD-3-Clause |
def construct_grids(batch):
"""Construct the map grid from the batch object
Parameters
----------
batch : Batch object
The object returned by :func:`fetch_species_distributions`
Returns
-------
(xgrid, ygrid) : 1-D arrays
The grid corresponding to the values in batch.covera... | Construct the map grid from the batch object
Parameters
----------
batch : Batch object
The object returned by :func:`fetch_species_distributions`
Returns
-------
(xgrid, ygrid) : 1-D arrays
The grid corresponding to the values in batch.coverages
| construct_grids | python | scikit-learn/scikit-learn | examples/applications/plot_species_distribution_modeling.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/applications/plot_species_distribution_modeling.py | BSD-3-Clause |
def create_species_bunch(species_name, train, test, coverages, xgrid, ygrid):
"""Create a bunch with information about a particular organism
This will use the test/train record arrays to extract the
data specific to the given species name.
"""
bunch = Bunch(name=" ".join(species_name.split("_")[:2]... | Create a bunch with information about a particular organism
This will use the test/train record arrays to extract the
data specific to the given species name.
| create_species_bunch | python | scikit-learn/scikit-learn | examples/applications/plot_species_distribution_modeling.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/applications/plot_species_distribution_modeling.py | BSD-3-Clause |
def build_projection_operator(l_x, n_dir):
"""Compute the tomography design matrix.
Parameters
----------
l_x : int
linear size of image array
n_dir : int
number of angles at which projections are acquired.
Returns
-------
p : sparse matrix of shape (n_dir l_x, l_x**2... | Compute the tomography design matrix.
Parameters
----------
l_x : int
linear size of image array
n_dir : int
number of angles at which projections are acquired.
Returns
-------
p : sparse matrix of shape (n_dir l_x, l_x**2)
| build_projection_operator | python | scikit-learn/scikit-learn | examples/applications/plot_tomography_l1_reconstruction.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/applications/plot_tomography_l1_reconstruction.py | BSD-3-Clause |
def index(redirects, index_map, k):
"""Find the index of an article name after redirect resolution"""
k = redirects.get(k, k)
return index_map.setdefault(k, len(index_map)) | Find the index of an article name after redirect resolution | index | python | scikit-learn/scikit-learn | examples/applications/wikipedia_principal_eigenvector.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/applications/wikipedia_principal_eigenvector.py | BSD-3-Clause |
def get_redirects(redirects_filename):
"""Parse the redirections and build a transitively closed map out of it"""
redirects = {}
print("Parsing the NT redirect file")
for l, line in enumerate(BZ2File(redirects_filename)):
split = line.split()
if len(split) != 4:
print("ignori... | Parse the redirections and build a transitively closed map out of it | get_redirects | python | scikit-learn/scikit-learn | examples/applications/wikipedia_principal_eigenvector.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/applications/wikipedia_principal_eigenvector.py | BSD-3-Clause |
def get_adjacency_matrix(redirects_filename, page_links_filename, limit=None):
"""Extract the adjacency graph as a scipy sparse matrix
Redirects are resolved first.
Returns X, the scipy sparse adjacency matrix, redirects as python
dict from article names to article names and index_map a python dict
... | Extract the adjacency graph as a scipy sparse matrix
Redirects are resolved first.
Returns X, the scipy sparse adjacency matrix, redirects as python
dict from article names to article names and index_map a python dict
from article names to python int (article indexes).
| get_adjacency_matrix | python | scikit-learn/scikit-learn | examples/applications/wikipedia_principal_eigenvector.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/applications/wikipedia_principal_eigenvector.py | BSD-3-Clause |
def predict_proba(self, X):
"""Min-max scale output of `decision_function` to [0, 1]."""
df = self.decision_function(X)
calibrated_df = (df - self.df_min_) / (self.df_max_ - self.df_min_)
proba_pos_class = np.clip(calibrated_df, 0, 1)
proba_neg_class = 1 - proba_pos_class
... | Min-max scale output of `decision_function` to [0, 1]. | predict_proba | python | scikit-learn/scikit-learn | examples/calibration/plot_calibration_curve.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/calibration/plot_calibration_curve.py | BSD-3-Clause |
def predict_proba(self, X):
"""Min-max scale output of `decision_function` to [0,1]."""
df = self.decision_function(X)
calibrated_df = (df - self.df_min_) / (self.df_max_ - self.df_min_)
proba_pos_class = np.clip(calibrated_df, 0, 1)
proba_neg_class = 1 - proba_pos_class
... | Min-max scale output of `decision_function` to [0,1]. | predict_proba | python | scikit-learn/scikit-learn | examples/calibration/plot_compare_calibration.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/calibration/plot_compare_calibration.py | BSD-3-Clause |
def generate_data(n_samples, n_features):
"""Generate random blob-ish data with noisy features.
This returns an array of input data with shape `(n_samples, n_features)`
and an array of `n_samples` target labels.
Only one feature contains discriminative information, the other features
contain only ... | Generate random blob-ish data with noisy features.
This returns an array of input data with shape `(n_samples, n_features)`
and an array of `n_samples` target labels.
Only one feature contains discriminative information, the other features
contain only noise.
| generate_data | python | scikit-learn/scikit-learn | examples/classification/plot_lda.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/classification/plot_lda.py | BSD-3-Clause |
def _classifier_has(attr):
"""Check if we can delegate a method to the underlying classifier.
First, we check the first fitted classifier if available, otherwise we
check the unfitted classifier.
"""
return lambda estimator: (
hasattr(estimator.classifier_, attr)
if hasattr(estimato... | Check if we can delegate a method to the underlying classifier.
First, we check the first fitted classifier if available, otherwise we
check the unfitted classifier.
| _classifier_has | python | scikit-learn/scikit-learn | examples/cluster/plot_inductive_clustering.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/cluster/plot_inductive_clustering.py | BSD-3-Clause |
def bench_k_means(kmeans, name, data, labels):
"""Benchmark to evaluate the KMeans initialization methods.
Parameters
----------
kmeans : KMeans instance
A :class:`~sklearn.cluster.KMeans` instance with the initialization
already set.
name : str
Name given to the strategy. I... | Benchmark to evaluate the KMeans initialization methods.
Parameters
----------
kmeans : KMeans instance
A :class:`~sklearn.cluster.KMeans` instance with the initialization
already set.
name : str
Name given to the strategy. It will be used to show the results in a
table.... | bench_k_means | python | scikit-learn/scikit-learn | examples/cluster/plot_kmeans_digits.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/cluster/plot_kmeans_digits.py | BSD-3-Clause |
def ricker_function(resolution, center, width):
"""Discrete sub-sampled Ricker (Mexican hat) wavelet"""
x = np.linspace(0, resolution - 1, resolution)
x = (
(2 / (np.sqrt(3 * width) * np.pi**0.25))
* (1 - (x - center) ** 2 / width**2)
* np.exp(-((x - center) ** 2) / (2 * width**2))
... | Discrete sub-sampled Ricker (Mexican hat) wavelet | ricker_function | python | scikit-learn/scikit-learn | examples/decomposition/plot_sparse_coding.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/decomposition/plot_sparse_coding.py | BSD-3-Clause |
def ricker_matrix(width, resolution, n_components):
"""Dictionary of Ricker (Mexican hat) wavelets"""
centers = np.linspace(0, resolution - 1, n_components)
D = np.empty((n_components, resolution))
for i, center in enumerate(centers):
D[i] = ricker_function(resolution, center, width)
D /= np... | Dictionary of Ricker (Mexican hat) wavelets | ricker_matrix | python | scikit-learn/scikit-learn | examples/decomposition/plot_sparse_coding.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/decomposition/plot_sparse_coding.py | BSD-3-Clause |
def fit(self, X, y):
"""
Fit the estimator to the training data.
"""
self.classes_ = sorted(set(y))
# Custom attribute to track if the estimator is fitted
self._is_fitted = True
return self |
Fit the estimator to the training data.
| fit | python | scikit-learn/scikit-learn | examples/developing_estimators/sklearn_is_fitted.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/developing_estimators/sklearn_is_fitted.py | BSD-3-Clause |
def predict(self, X):
"""
Perform Predictions
If the estimator is not fitted, then raise NotFittedError
"""
check_is_fitted(self)
# Perform prediction logic
predictions = [self.classes_[0]] * len(X)
return predictions |
Perform Predictions
If the estimator is not fitted, then raise NotFittedError
| predict | python | scikit-learn/scikit-learn | examples/developing_estimators/sklearn_is_fitted.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/developing_estimators/sklearn_is_fitted.py | BSD-3-Clause |
def score(self, X, y):
"""
Calculate Score
If the estimator is not fitted, then raise NotFittedError
"""
check_is_fitted(self)
# Perform scoring logic
return 0.5 |
Calculate Score
If the estimator is not fitted, then raise NotFittedError
| score | python | scikit-learn/scikit-learn | examples/developing_estimators/sklearn_is_fitted.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/developing_estimators/sklearn_is_fitted.py | BSD-3-Clause |
def _f(self, s1, s2):
"""
kernel value between a pair of sequences
"""
return sum(
[1.0 if c1 == c2 else self.baseline_similarity for c1 in s1 for c2 in s2]
) |
kernel value between a pair of sequences
| _f | python | scikit-learn/scikit-learn | examples/gaussian_process/plot_gpr_on_structured_data.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/gaussian_process/plot_gpr_on_structured_data.py | BSD-3-Clause |
def plot_gpr_samples(gpr_model, n_samples, ax):
"""Plot samples drawn from the Gaussian process model.
If the Gaussian process model is not trained then the drawn samples are
drawn from the prior distribution. Otherwise, the samples are drawn from
the posterior distribution. Be aware that a sample here... | Plot samples drawn from the Gaussian process model.
If the Gaussian process model is not trained then the drawn samples are
drawn from the prior distribution. Otherwise, the samples are drawn from
the posterior distribution. Be aware that a sample here corresponds to a
function.
Parameters
---... | plot_gpr_samples | python | scikit-learn/scikit-learn | examples/gaussian_process/plot_gpr_prior_posterior.py | https://github.com/scikit-learn/scikit-learn/blob/master/examples/gaussian_process/plot_gpr_prior_posterior.py | BSD-3-Clause |
Subsets and Splits
Django Code with Docstrings
Filters Python code examples from Django repository that contain Django-related code, helping identify relevant code snippets for understanding Django framework usage patterns.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves Python code examples from Django repository that contain 'django' in the code, which helps identify Django-specific code snippets but provides limited analytical insights beyond basic filtering.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves specific code examples from the Flask repository but doesn't provide meaningful analysis or patterns beyond basic data retrieval.
HTTPX Repo Code and Docstrings
Retrieves specific code examples from the httpx repository, which is useful for understanding how particular libraries are used but doesn't provide broader analytical insights about the dataset.
Requests Repo Docstrings & Code
Retrieves code examples with their docstrings and file paths from the requests repository, providing basic filtering but limited analytical value beyond finding specific code samples.
Quart Repo Docstrings & Code
Retrieves code examples with their docstrings from the Quart repository, providing basic code samples but offering limited analytical value for understanding broader patterns or relationships in the dataset.