_id stringlengths 5 9 | text stringlengths 5 385k | title stringclasses 1
value |
|---|---|---|
doc_28500 | Return the value of the numeric capability corresponding to the terminfo capability name capname as an integer. Return the value -2 if capname is not a numeric capability, or -1 if it is canceled or absent from the terminal description. | |
doc_28501 |
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | |
doc_28502 | Return an int with k random bits. | |
doc_28503 | Unpacker is the complementary class which unpacks XDR data values from a string buffer. The input buffer is given as data. | |
doc_28504 |
Return the standard vertical stem width as float, or None if not specified in AFM file. | |
doc_28505 | Pickles the record’s attribute dictionary and writes it to the socket in binary format. If there is an error with the socket, silently drops the packet. If the connection was previously lost, re-establishes the connection. To unpickle the record at the receiving end into a LogRecord, use the makeLogRecord() function. | |
doc_28506 | See Migration guide for more details. tf.compat.v1.sparse.reorder, tf.compat.v1.sparse_reorder
tf.sparse.reorder(
sp_input, name=None
)
Note that by convention, all sparse ops preserve the canonical ordering along increasing dimension number. The only time ordering can be violated is during manual manipulation of the indices and values to add entries. Reordering does not affect the shape of the SparseTensor. For example, if sp_input has shape [4, 5] and indices / values: [0, 3]: b
[0, 1]: a
[3, 1]: d
[2, 0]: c
then the output will be a SparseTensor of shape [4, 5] and indices / values: [0, 1]: a
[0, 3]: b
[2, 0]: c
[3, 1]: d
Args
sp_input The input SparseTensor.
name A name prefix for the returned tensors (optional)
Returns A SparseTensor with the same shape and non-empty values, but in canonical ordering.
Raises
TypeError If sp_input is not a SparseTensor. | |
doc_28507 | tf.compat.v1.summary.histogram(
name, values, collections=None, family=None
)
Adding a histogram summary makes it possible to visualize your data's distribution in TensorBoard. You can see a detailed explanation of the TensorBoard histogram dashboard here. The generated Summary has one summary value containing a histogram for values. This op reports an InvalidArgument error if any value is not finite.
Args
name A name for the generated node. Will also serve as a series name in TensorBoard.
values A real numeric Tensor. Any shape. Values to use to build the histogram.
collections Optional list of graph collections keys. The new summary op is added to these collections. Defaults to [GraphKeys.SUMMARIES].
family Optional; if provided, used as the prefix of the summary tag name, which controls the tab name used for display on Tensorboard.
Returns A scalar Tensor of type string. The serialized Summary protocol buffer. | |
doc_28508 | accessor for ‘no-transform’ | |
doc_28509 | Decodes object (taking the current state of the decoder into account) and returns the resulting decoded object. If this is the last call to decode() final must be true (the default is false). If final is true the decoder must decode the input completely and must flush all buffers. If this isn’t possible (e.g. because of incomplete byte sequences at the end of the input) it must initiate error handling just like in the stateless case (which might raise an exception). | |
doc_28510 | Returns the date this session will expire. For sessions with no custom expiration (or those set to expire at browser close), this will equal the date SESSION_COOKIE_AGE seconds from now. This function accepts the same keyword arguments as get_expiry_age(). | |
doc_28511 | New in Django 4.0. Returns the URL to redirect to after login. The default implementation resolves and returns next_page if set, or LOGIN_REDIRECT_URL otherwise. | |
doc_28512 | This function prints out a given traceback and exception to sys.stderr. When an exception is raised and uncaught, the interpreter calls sys.excepthook with three arguments, the exception class, exception instance, and a traceback object. In an interactive session this happens just before control is returned to the prompt; in a Python program this happens just before the program exits. The handling of such top-level exceptions can be customized by assigning another three-argument function to sys.excepthook.
Raise an auditing event sys.excepthook with arguments hook, type, value, traceback when an uncaught exception occurs. If no hook has been set, hook may be None. If any hook raises an exception derived from RuntimeError the call to the hook will be suppressed. Otherwise, the audit hook exception will be reported as unraisable and sys.excepthook will be called. See also The sys.unraisablehook() function handles unraisable exceptions and the threading.excepthook() function handles exception raised by threading.Thread.run(). | |
doc_28513 |
Return white top hat of an image. The white top hat of an image is defined as the image minus its morphological opening. This operation returns the bright spots of the image that are smaller than the structuring element. Parameters
imagendarray
Image array.
selemndarray, optional
The neighborhood expressed as an array of 1’s and 0’s. If None, use cross-shaped structuring element (connectivity=1).
outndarray, optional
The array to store the result of the morphology. If None is passed, a new array will be allocated. Returns
outarray, same shape and type as image
The result of the morphological white top hat. See also
black_tophat
References
1
https://en.wikipedia.org/wiki/Top-hat_transform Examples >>> # Subtract grey background from bright peak
>>> import numpy as np
>>> from skimage.morphology import square
>>> bright_on_grey = np.array([[2, 3, 3, 3, 2],
... [3, 4, 5, 4, 3],
... [3, 5, 9, 5, 3],
... [3, 4, 5, 4, 3],
... [2, 3, 3, 3, 2]], dtype=np.uint8)
>>> white_tophat(bright_on_grey, square(3))
array([[0, 0, 0, 0, 0],
[0, 0, 1, 0, 0],
[0, 1, 5, 1, 0],
[0, 0, 1, 0, 0],
[0, 0, 0, 0, 0]], dtype=uint8) | |
doc_28514 |
Error raised when problems arise during merging due to problems with input data. Subclass of ValueError. | |
doc_28515 |
Reduce X to the selected features. Parameters
Xarray of shape [n_samples, n_features]
The input samples. Returns
X_rarray of shape [n_samples, n_selected_features]
The input samples with only the selected features. | |
doc_28516 |
Return a Path of the right half of a unit circle. See Path.circle for the reference on the approximation used. | |
doc_28517 |
Return the image mask and the histogram data. Returns
maskarray of bool, same shape as image
The selected pixels.
datadict
The data describing the histogram and the selected region. The dictionary contains: ‘bins’ : array of float The bin boundaries for both a and b channels. ‘hist’ : 2D array of float The normalized histogram. ‘edges’ : tuple of array of float The bin edges along each dimension ‘extents’ : tuple of float The left and right and top and bottom of the selected region. | |
doc_28518 | os.MFD_ALLOW_SEALING
os.MFD_HUGETLB
os.MFD_HUGE_SHIFT
os.MFD_HUGE_MASK
os.MFD_HUGE_64KB
os.MFD_HUGE_512KB
os.MFD_HUGE_1MB
os.MFD_HUGE_2MB
os.MFD_HUGE_8MB
os.MFD_HUGE_16MB
os.MFD_HUGE_32MB
os.MFD_HUGE_256MB
os.MFD_HUGE_512MB
os.MFD_HUGE_1GB
os.MFD_HUGE_2GB
os.MFD_HUGE_16GB
These flags can be passed to memfd_create(). Availability: Linux 3.17 or newer with glibc 2.27 or newer. The MFD_HUGE* flags are only available since Linux 4.14. New in version 3.8. | |
doc_28519 | See Migration guide for more details. tf.compat.v1.keras.utils.OrderedEnqueuer
tf.keras.utils.OrderedEnqueuer(
sequence, use_multiprocessing=False, shuffle=False
)
Used in fit_generator, evaluate_generator, predict_generator.
Arguments
sequence A tf.keras.utils.data_utils.Sequence object.
use_multiprocessing use multiprocessing if True, otherwise threading
shuffle whether to shuffle the data at the beginning of each epoch Methods get View source
get()
Creates a generator to extract data from the queue. Skip the data if it is None.
Yields The next element in the queue, i.e. a tuple (inputs, targets) or (inputs, targets, sample_weights).
is_running View source
is_running()
start View source
start(
workers=1, max_queue_size=10
)
Starts the handler's workers.
Arguments
workers Number of workers.
max_queue_size queue size (when full, workers could block on put()) stop View source
stop(
timeout=None
)
Stops running threads and wait for them to exit, if necessary. Should be called by the same thread which called start().
Arguments
timeout maximum time to wait on thread.join() | |
doc_28520 | A boolean value that determines whether to show the navigation sidebar on larger screens. By default, it is set to True. | |
doc_28521 |
Add other to self, and return a new masked array. | |
doc_28522 | Returns a date object containing the first day of the year after the date provided. This function can also return None or raise an Http404 exception, depending on the values of allow_empty and allow_future. | |
doc_28523 |
Return a copy of the array. Parameters
order{‘C’, ‘F’, ‘A’, ‘K’}, optional
Controls the memory layout of the copy. ‘C’ means C-order, ‘F’ means F-order, ‘A’ means ‘F’ if a is Fortran contiguous, ‘C’ otherwise. ‘K’ means match the layout of a as closely as possible. (Note that this function and numpy.copy are very similar but have different default values for their order= arguments, and this function always passes sub-classes through.) See also numpy.copy
Similar function with different default behavior numpy.copyto
Notes This function is the preferred method for creating an array copy. The function numpy.copy is similar, but it defaults to using order ‘K’, and will not pass sub-classes through by default. Examples >>> x = np.array([[1,2,3],[4,5,6]], order='F')
>>> y = x.copy()
>>> x.fill(0)
>>> x
array([[0, 0, 0],
[0, 0, 0]])
>>> y
array([[1, 2, 3],
[4, 5, 6]])
>>> y.flags['C_CONTIGUOUS']
True | |
doc_28524 | tf.reduce_mean
tf.math.reduce_mean(
input_tensor, axis=None, keepdims=False, name=None
)
Reduces input_tensor along the dimensions given in axis by computing the mean of elements across the dimensions in axis. Unless keepdims is true, the rank of the tensor is reduced by 1 for each of the entries in axis, which must be unique. If keepdims is true, the reduced dimensions are retained with length 1. If axis is None, all dimensions are reduced, and a tensor with a single element is returned. For example:
x = tf.constant([[1., 1.], [2., 2.]])
tf.reduce_mean(x)
<tf.Tensor: shape=(), dtype=float32, numpy=1.5>
tf.reduce_mean(x, 0)
<tf.Tensor: shape=(2,), dtype=float32, numpy=array([1.5, 1.5], dtype=float32)>
tf.reduce_mean(x, 1)
<tf.Tensor: shape=(2,), dtype=float32, numpy=array([1., 2.], dtype=float32)>
Args
input_tensor The tensor to reduce. Should have numeric type.
axis The dimensions to reduce. If None (the default), reduces all dimensions. Must be in the range [-rank(input_tensor), rank(input_tensor)).
keepdims If true, retains reduced dimensions with length 1.
name A name for the operation (optional).
Returns The reduced tensor.
Numpy Compatibility Equivalent to np.mean Please note that np.mean has a dtype parameter that could be used to specify the output type. By default this is dtype=float64. On the other hand, tf.reduce_mean has an aggressive type inference from input_tensor, for example:
x = tf.constant([1, 0, 1, 0])
tf.reduce_mean(x)
<tf.Tensor: shape=(), dtype=int32, numpy=0>
y = tf.constant([1., 0., 1., 0.])
tf.reduce_mean(y)
<tf.Tensor: shape=(), dtype=float32, numpy=0.5> | |
doc_28525 |
Use this light source to adjust the colors of the rgb input array to give the impression of a shaded relief map with the given elevation. Parameters
rgbarray-like
An (M, N, 3) RGB array, assumed to be in the range of 0 to 1.
elevationarray-like
An (M, N) array of the height values used to generate a shaded map.
fractionnumber
Increases or decreases the contrast of the hillshade. Values greater than one will cause intermediate values to move closer to full illumination or shadow (and clipping any values that move beyond 0 or 1). Note that this is not visually or mathematically the same as vertical exaggeration.
blend_mode{'hsv', 'overlay', 'soft'} or callable, optional
The type of blending used to combine the colormapped data values with the illumination intensity. For backwards compatibility, this defaults to "hsv". Note that for most topographic surfaces, "overlay" or "soft" appear more visually realistic. If a user-defined function is supplied, it is expected to combine an MxNx3 RGB array of floats (ranging 0 to 1) with an MxNx1 hillshade array (also 0 to 1). (Call signature func(rgb, illum, **kwargs)) Additional kwargs supplied to this function will be passed on to the blend_mode function.
vert_exagnumber, optional
The amount to exaggerate the elevation values by when calculating illumination. This can be used either to correct for differences in units between the x-y coordinate system and the elevation coordinate system (e.g. decimal degrees vs. meters) or to exaggerate or de-emphasize topography.
dxnumber, optional
The x-spacing (columns) of the input elevation grid.
dynumber, optional
The y-spacing (rows) of the input elevation grid. Additional kwargs are passed on to the *blend_mode* function.
Returns
ndarray
An (m, n, 3) array of floats ranging between 0-1. | |
doc_28526 | The base exception class. Error has a single public attribute msg containing the description of the error. | |
doc_28527 | Null-terminated string containing references to environment variables (%PATH%). | |
doc_28528 |
Tuple of bytes to step in each dimension when traversing an array. The byte offset of element (i[0], i[1], ..., i[n]) in an array a is: offset = sum(np.array(i) * a.strides)
A more detailed explanation of strides can be found in the “ndarray.rst” file in the NumPy reference guide. See also numpy.lib.stride_tricks.as_strided
Notes Imagine an array of 32-bit integers (each 4 bytes): x = np.array([[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9]], dtype=np.int32)
This array is stored in memory as 40 bytes, one after the other (known as a contiguous block of memory). The strides of an array tell us how many bytes we have to skip in memory to move to the next position along a certain axis. For example, we have to skip 4 bytes (1 value) to move to the next column, but 20 bytes (5 values) to get to the same position in the next row. As such, the strides for the array x will be (20, 4). Examples >>> y = np.reshape(np.arange(2*3*4), (2,3,4))
>>> y
array([[[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]],
[[12, 13, 14, 15],
[16, 17, 18, 19],
[20, 21, 22, 23]]])
>>> y.strides
(48, 16, 4)
>>> y[1,1,1]
17
>>> offset=sum(y.strides * np.array((1,1,1)))
>>> offset/y.itemsize
17
>>> x = np.reshape(np.arange(5*6*7*8), (5,6,7,8)).transpose(2,3,1,0)
>>> x.strides
(32, 4, 224, 1344)
>>> i = np.array([3,5,2,2])
>>> offset = sum(i * x.strides)
>>> x[3,5,2,2]
813
>>> offset / x.itemsize
813 | |
doc_28529 |
Return the list of major Ticks. | |
doc_28530 | Points to the model the field relates to. For example, Author in ForeignKey(Author, on_delete=models.CASCADE). The related_model for a GenericForeignKey is always None. | |
doc_28531 |
Time Series cross-validator Provides train/test indices to split time series data samples that are observed at fixed time intervals, in train/test sets. In each split, test indices must be higher than before, and thus shuffling in cross validator is inappropriate. This cross-validation object is a variation of KFold. In the kth split, it returns first k folds as train set and the (k+1)th fold as test set. Note that unlike standard cross-validation methods, successive training sets are supersets of those that come before them. Read more in the User Guide. New in version 0.18. Parameters
n_splitsint, default=5
Number of splits. Must be at least 2. Changed in version 0.22: n_splits default value changed from 3 to 5.
max_train_sizeint, default=None
Maximum size for a single training set.
test_sizeint, default=None
Used to limit the size of the test set. Defaults to n_samples // (n_splits + 1), which is the maximum allowed value with gap=0. New in version 0.24.
gapint, default=0
Number of samples to exclude from the end of each train set before the test set. New in version 0.24. Notes The training set has size i * n_samples // (n_splits + 1)
+ n_samples % (n_splits + 1) in the i th split, with a test set of size n_samples//(n_splits + 1) by default, where n_samples is the number of samples. Examples >>> import numpy as np
>>> from sklearn.model_selection import TimeSeriesSplit
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([1, 2, 3, 4, 5, 6])
>>> tscv = TimeSeriesSplit()
>>> print(tscv)
TimeSeriesSplit(gap=0, max_train_size=None, n_splits=5, test_size=None)
>>> for train_index, test_index in tscv.split(X):
... print("TRAIN:", train_index, "TEST:", test_index)
... X_train, X_test = X[train_index], X[test_index]
... y_train, y_test = y[train_index], y[test_index]
TRAIN: [0] TEST: [1]
TRAIN: [0 1] TEST: [2]
TRAIN: [0 1 2] TEST: [3]
TRAIN: [0 1 2 3] TEST: [4]
TRAIN: [0 1 2 3 4] TEST: [5]
>>> # Fix test_size to 2 with 12 samples
>>> X = np.random.randn(12, 2)
>>> y = np.random.randint(0, 2, 12)
>>> tscv = TimeSeriesSplit(n_splits=3, test_size=2)
>>> for train_index, test_index in tscv.split(X):
... print("TRAIN:", train_index, "TEST:", test_index)
... X_train, X_test = X[train_index], X[test_index]
... y_train, y_test = y[train_index], y[test_index]
TRAIN: [0 1 2 3 4 5] TEST: [6 7]
TRAIN: [0 1 2 3 4 5 6 7] TEST: [8 9]
TRAIN: [0 1 2 3 4 5 6 7 8 9] TEST: [10 11]
>>> # Add in a 2 period gap
>>> tscv = TimeSeriesSplit(n_splits=3, test_size=2, gap=2)
>>> for train_index, test_index in tscv.split(X):
... print("TRAIN:", train_index, "TEST:", test_index)
... X_train, X_test = X[train_index], X[test_index]
... y_train, y_test = y[train_index], y[test_index]
TRAIN: [0 1 2 3] TEST: [6 7]
TRAIN: [0 1 2 3 4 5] TEST: [8 9]
TRAIN: [0 1 2 3 4 5 6 7] TEST: [10 11]
Methods
get_n_splits([X, y, groups]) Returns the number of splitting iterations in the cross-validator
split(X[, y, groups]) Generate indices to split data into training and test set.
get_n_splits(X=None, y=None, groups=None) [source]
Returns the number of splitting iterations in the cross-validator Parameters
Xobject
Always ignored, exists for compatibility.
yobject
Always ignored, exists for compatibility.
groupsobject
Always ignored, exists for compatibility. Returns
n_splitsint
Returns the number of splitting iterations in the cross-validator.
split(X, y=None, groups=None) [source]
Generate indices to split data into training and test set. Parameters
Xarray-like of shape (n_samples, n_features)
Training data, where n_samples is the number of samples and n_features is the number of features.
yarray-like of shape (n_samples,)
Always ignored, exists for compatibility.
groupsarray-like of shape (n_samples,)
Always ignored, exists for compatibility. Yields
trainndarray
The training set indices for that split.
testndarray
The testing set indices for that split. | |
doc_28532 | Primary key. The field itself may contain up to 40 characters. The current implementation generates a 32-character string (a random sequence of digits and lowercase ASCII letters). | |
doc_28533 | Equivalent to put(item), provided for compatibility with Queue.put_nowait(). | |
doc_28534 | 'blogs.blog': lambda o: "/blogs/%s/" % o.slug,
'news.story': lambda o: "/stories/%s/%s/" % (o.pub_year, o.slug),
}
The model name used in this setting should be all lowercase, regardless of the case of the actual model class name. ADMINS Default: [] (Empty list) A list of all the people who get code error notifications. When DEBUG=False and AdminEmailHandler is configured in LOGGING (done by default), Django emails these people the details of exceptions raised in the request/response cycle. Each item in the list should be a tuple of (Full name, email address). Example: [('John', 'john@example.com'), ('Mary', 'mary@example.com')]
ALLOWED_HOSTS Default: [] (Empty list) A list of strings representing the host/domain names that this Django site can serve. This is a security measure to prevent HTTP Host header attacks, which are possible even under many seemingly-safe web server configurations. Values in this list can be fully qualified names (e.g. 'www.example.com'), in which case they will be matched against the request’s Host header exactly (case-insensitive, not including port). A value beginning with a period can be used as a subdomain wildcard: '.example.com' will match example.com, www.example.com, and any other subdomain of example.com. A value of '*' will match anything; in this case you are responsible to provide your own validation of the Host header (perhaps in a middleware; if so this middleware must be listed first in MIDDLEWARE). Django also allows the fully qualified domain name (FQDN) of any entries. Some browsers include a trailing dot in the Host header which Django strips when performing host validation. If the Host header (or X-Forwarded-Host if USE_X_FORWARDED_HOST is enabled) does not match any value in this list, the django.http.HttpRequest.get_host() method will raise SuspiciousOperation. When DEBUG is True and ALLOWED_HOSTS is empty, the host is validated against ['.localhost', '127.0.0.1', '[::1]']. ALLOWED_HOSTS is also checked when running tests. This validation only applies via get_host(); if your code accesses the Host header directly from request.META you are bypassing this security protection. APPEND_SLASH Default: True When set to True, if the request URL does not match any of the patterns in the URLconf and it doesn’t end in a slash, an HTTP redirect is issued to the same URL with a slash appended. Note that the redirect may cause any data submitted in a POST request to be lost. The APPEND_SLASH setting is only used if CommonMiddleware is installed (see Middleware). See also PREPEND_WWW. CACHES Default: {
'default': {
'BACKEND': 'django.core.cache.backends.locmem.LocMemCache',
}
}
A dictionary containing the settings for all caches to be used with Django. It is a nested dictionary whose contents maps cache aliases to a dictionary containing the options for an individual cache. The CACHES setting must configure a default cache; any number of additional caches may also be specified. If you are using a cache backend other than the local memory cache, or you need to define multiple caches, other options will be required. The following cache options are available. BACKEND Default: '' (Empty string) The cache backend to use. The built-in cache backends are: 'django.core.cache.backends.db.DatabaseCache' 'django.core.cache.backends.dummy.DummyCache' 'django.core.cache.backends.filebased.FileBasedCache' 'django.core.cache.backends.locmem.LocMemCache' 'django.core.cache.backends.memcached.PyMemcacheCache' 'django.core.cache.backends.memcached.PyLibMCCache' 'django.core.cache.backends.redis.RedisCache' You can use a cache backend that doesn’t ship with Django by setting BACKEND to a fully-qualified path of a cache backend class (i.e. mypackage.backends.whatever.WhateverCache). Changed in Django 3.2: The PyMemcacheCache backend was added. Changed in Django 4.0: The RedisCache backend was added. KEY_FUNCTION A string containing a dotted path to a function (or any callable) that defines how to compose a prefix, version and key into a final cache key. The default implementation is equivalent to the function: def make_key(key, key_prefix, version):
return ':'.join([key_prefix, str(version), key])
You may use any key function you want, as long as it has the same argument signature. See the cache documentation for more information. KEY_PREFIX Default: '' (Empty string) A string that will be automatically included (prepended by default) to all cache keys used by the Django server. See the cache documentation for more information. LOCATION Default: '' (Empty string) The location of the cache to use. This might be the directory for a file system cache, a host and port for a memcache server, or an identifying name for a local memory cache. e.g.: CACHES = {
'default': {
'BACKEND': 'django.core.cache.backends.filebased.FileBasedCache',
'LOCATION': '/var/tmp/django_cache',
}
}
OPTIONS Default: None Extra parameters to pass to the cache backend. Available parameters vary depending on your cache backend. Some information on available parameters can be found in the cache arguments documentation. For more information, consult your backend module’s own documentation. TIMEOUT Default: 300 The number of seconds before a cache entry is considered stale. If the value of this setting is None, cache entries will not expire. A value of 0 causes keys to immediately expire (effectively “don’t cache”). VERSION Default: 1 The default version number for cache keys generated by the Django server. See the cache documentation for more information. CACHE_MIDDLEWARE_ALIAS Default: 'default' The cache connection to use for the cache middleware. CACHE_MIDDLEWARE_KEY_PREFIX Default: '' (Empty string) A string which will be prefixed to the cache keys generated by the cache middleware. This prefix is combined with the KEY_PREFIX setting; it does not replace it. See Django’s cache framework. CACHE_MIDDLEWARE_SECONDS Default: 600 The default number of seconds to cache a page for the cache middleware. See Django’s cache framework. CSRF_COOKIE_AGE Default: 31449600 (approximately 1 year, in seconds) The age of CSRF cookies, in seconds. The reason for setting a long-lived expiration time is to avoid problems in the case of a user closing a browser or bookmarking a page and then loading that page from a browser cache. Without persistent cookies, the form submission would fail in this case. Some browsers (specifically Internet Explorer) can disallow the use of persistent cookies or can have the indexes to the cookie jar corrupted on disk, thereby causing CSRF protection checks to (sometimes intermittently) fail. Change this setting to None to use session-based CSRF cookies, which keep the cookies in-memory instead of on persistent storage. CSRF_COOKIE_DOMAIN Default: None The domain to be used when setting the CSRF cookie. This can be useful for easily allowing cross-subdomain requests to be excluded from the normal cross site request forgery protection. It should be set to a string such as ".example.com" to allow a POST request from a form on one subdomain to be accepted by a view served from another subdomain. Please note that the presence of this setting does not imply that Django’s CSRF protection is safe from cross-subdomain attacks by default - please see the CSRF limitations section. CSRF_COOKIE_HTTPONLY Default: False Whether to use HttpOnly flag on the CSRF cookie. If this is set to True, client-side JavaScript will not be able to access the CSRF cookie. Designating the CSRF cookie as HttpOnly doesn’t offer any practical protection because CSRF is only to protect against cross-domain attacks. If an attacker can read the cookie via JavaScript, they’re already on the same domain as far as the browser knows, so they can do anything they like anyway. (XSS is a much bigger hole than CSRF.) Although the setting offers little practical benefit, it’s sometimes required by security auditors. If you enable this and need to send the value of the CSRF token with an AJAX request, your JavaScript must pull the value from a hidden CSRF token form input instead of from the cookie. See SESSION_COOKIE_HTTPONLY for details on HttpOnly. CSRF_COOKIE_NAME Default: 'csrftoken' The name of the cookie to use for the CSRF authentication token. This can be whatever you want (as long as it’s different from the other cookie names in your application). See Cross Site Request Forgery protection. CSRF_COOKIE_PATH Default: '/' The path set on the CSRF cookie. This should either match the URL path of your Django installation or be a parent of that path. This is useful if you have multiple Django instances running under the same hostname. They can use different cookie paths, and each instance will only see its own CSRF cookie. CSRF_COOKIE_SAMESITE Default: 'Lax' The value of the SameSite flag on the CSRF cookie. This flag prevents the cookie from being sent in cross-site requests. See SESSION_COOKIE_SAMESITE for details about SameSite. CSRF_COOKIE_SECURE Default: False Whether to use a secure cookie for the CSRF cookie. If this is set to True, the cookie will be marked as “secure”, which means browsers may ensure that the cookie is only sent with an HTTPS connection. CSRF_USE_SESSIONS Default: False Whether to store the CSRF token in the user’s session instead of in a cookie. It requires the use of django.contrib.sessions. Storing the CSRF token in a cookie (Django’s default) is safe, but storing it in the session is common practice in other web frameworks and therefore sometimes demanded by security auditors. Since the default error views require the CSRF token, SessionMiddleware must appear in MIDDLEWARE before any middleware that may raise an exception to trigger an error view (such as PermissionDenied) if you’re using CSRF_USE_SESSIONS. See Middleware ordering. CSRF_FAILURE_VIEW Default: 'django.views.csrf.csrf_failure' A dotted path to the view function to be used when an incoming request is rejected by the CSRF protection. The function should have this signature: def csrf_failure(request, reason=""):
...
where reason is a short message (intended for developers or logging, not for end users) indicating the reason the request was rejected. It should return an HttpResponseForbidden. django.views.csrf.csrf_failure() accepts an additional template_name parameter that defaults to '403_csrf.html'. If a template with that name exists, it will be used to render the page. CSRF_HEADER_NAME Default: 'HTTP_X_CSRFTOKEN' The name of the request header used for CSRF authentication. As with other HTTP headers in request.META, the header name received from the server is normalized by converting all characters to uppercase, replacing any hyphens with underscores, and adding an 'HTTP_' prefix to the name. For example, if your client sends a 'X-XSRF-TOKEN' header, the setting should be 'HTTP_X_XSRF_TOKEN'. CSRF_TRUSTED_ORIGINS Default: [] (Empty list) A list of trusted origins for unsafe requests (e.g. POST). For requests that include the Origin header, Django’s CSRF protection requires that header match the origin present in the Host header. For a secure unsafe request that doesn’t include the Origin header, the request must have a Referer header that matches the origin present in the Host header. These checks prevent, for example, a POST request from subdomain.example.com from succeeding against api.example.com. If you need cross-origin unsafe requests, continuing the example, add 'https://subdomain.example.com' to this list (and/or http://... if requests originate from an insecure page). The setting also supports subdomains, so you could add 'https://*.example.com', for example, to allow access from all subdomains of example.com. Changed in Django 4.0: The values in older versions must only include the hostname (possibly with a leading dot) and not the scheme or an asterisk. Also, Origin header checking isn’t performed in older versions. DATABASES Default: {} (Empty dictionary) A dictionary containing the settings for all databases to be used with Django. It is a nested dictionary whose contents map a database alias to a dictionary containing the options for an individual database. The DATABASES setting must configure a default database; any number of additional databases may also be specified. The simplest possible settings file is for a single-database setup using SQLite. This can be configured using the following: DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': 'mydatabase',
}
}
When connecting to other database backends, such as MariaDB, MySQL, Oracle, or PostgreSQL, additional connection parameters will be required. See the ENGINE setting below on how to specify other database types. This example is for PostgreSQL: DATABASES = {
'default': {
'ENGINE': 'django.db.backends.postgresql',
'NAME': 'mydatabase',
'USER': 'mydatabaseuser',
'PASSWORD': 'mypassword',
'HOST': '127.0.0.1',
'PORT': '5432',
}
}
The following inner options that may be required for more complex configurations are available: ATOMIC_REQUESTS Default: False Set this to True to wrap each view in a transaction on this database. See Tying transactions to HTTP requests. AUTOCOMMIT Default: True Set this to False if you want to disable Django’s transaction management and implement your own. ENGINE Default: '' (Empty string) The database backend to use. The built-in database backends are: 'django.db.backends.postgresql' 'django.db.backends.mysql' 'django.db.backends.sqlite3' 'django.db.backends.oracle' You can use a database backend that doesn’t ship with Django by setting ENGINE to a fully-qualified path (i.e. mypackage.backends.whatever). HOST Default: '' (Empty string) Which host to use when connecting to the database. An empty string means localhost. Not used with SQLite. If this value starts with a forward slash ('/') and you’re using MySQL, MySQL will connect via a Unix socket to the specified socket. For example: "HOST": '/var/run/mysql'
If you’re using MySQL and this value doesn’t start with a forward slash, then this value is assumed to be the host. If you’re using PostgreSQL, by default (empty HOST), the connection to the database is done through UNIX domain sockets (‘local’ lines in pg_hba.conf). If your UNIX domain socket is not in the standard location, use the same value of unix_socket_directory from postgresql.conf. If you want to connect through TCP sockets, set HOST to ‘localhost’ or ‘127.0.0.1’ (‘host’ lines in pg_hba.conf). On Windows, you should always define HOST, as UNIX domain sockets are not available. NAME Default: '' (Empty string) The name of the database to use. For SQLite, it’s the full path to the database file. When specifying the path, always use forward slashes, even on Windows (e.g. C:/homes/user/mysite/sqlite3.db). CONN_MAX_AGE Default: 0 The lifetime of a database connection, as an integer of seconds. Use 0 to close database connections at the end of each request — Django’s historical behavior — and None for unlimited persistent connections. OPTIONS Default: {} (Empty dictionary) Extra parameters to use when connecting to the database. Available parameters vary depending on your database backend. Some information on available parameters can be found in the Database Backends documentation. For more information, consult your backend module’s own documentation. PASSWORD Default: '' (Empty string) The password to use when connecting to the database. Not used with SQLite. PORT Default: '' (Empty string) The port to use when connecting to the database. An empty string means the default port. Not used with SQLite. TIME_ZONE Default: None A string representing the time zone for this database connection or None. This inner option of the DATABASES setting accepts the same values as the general TIME_ZONE setting. When USE_TZ is True and this option is set, reading datetimes from the database returns aware datetimes in this time zone instead of UTC. When USE_TZ is False, it is an error to set this option.
If the database backend doesn’t support time zones (e.g. SQLite, MySQL, Oracle), Django reads and writes datetimes in local time according to this option if it is set and in UTC if it isn’t. Changing the connection time zone changes how datetimes are read from and written to the database. If Django manages the database and you don’t have a strong reason to do otherwise, you should leave this option unset. It’s best to store datetimes in UTC because it avoids ambiguous or nonexistent datetimes during daylight saving time changes. Also, receiving datetimes in UTC keeps datetime arithmetic simple — there’s no need to consider potential offset changes over a DST transition. If you’re connecting to a third-party database that stores datetimes in a local time rather than UTC, then you must set this option to the appropriate time zone. Likewise, if Django manages the database but third-party systems connect to the same database and expect to find datetimes in local time, then you must set this option.
If the database backend supports time zones (e.g. PostgreSQL), the TIME_ZONE option is very rarely needed. It can be changed at any time; the database takes care of converting datetimes to the desired time zone. Setting the time zone of the database connection may be useful for running raw SQL queries involving date/time functions provided by the database, such as date_trunc, because their results depend on the time zone. However, this has a downside: receiving all datetimes in local time makes datetime arithmetic more tricky — you must account for possible offset changes over DST transitions. Consider converting to local time explicitly with AT TIME ZONE in raw SQL queries instead of setting the TIME_ZONE option. DISABLE_SERVER_SIDE_CURSORS Default: False Set this to True if you want to disable the use of server-side cursors with QuerySet.iterator(). Transaction pooling and server-side cursors describes the use case. This is a PostgreSQL-specific setting. USER Default: '' (Empty string) The username to use when connecting to the database. Not used with SQLite. TEST Default: {} (Empty dictionary) A dictionary of settings for test databases; for more details about the creation and use of test databases, see The test database. Here’s an example with a test database configuration: DATABASES = {
'default': {
'ENGINE': 'django.db.backends.postgresql',
'USER': 'mydatabaseuser',
'NAME': 'mydatabase',
'TEST': {
'NAME': 'mytestdatabase',
},
},
}
The following keys in the TEST dictionary are available: CHARSET Default: None The character set encoding used to create the test database. The value of this string is passed directly through to the database, so its format is backend-specific. Supported by the PostgreSQL (postgresql) and MySQL (mysql) backends. COLLATION Default: None The collation order to use when creating the test database. This value is passed directly to the backend, so its format is backend-specific. Only supported for the mysql backend (see the MySQL manual for details). DEPENDENCIES Default: ['default'], for all databases other than default, which has no dependencies. The creation-order dependencies of the database. See the documentation on controlling the creation order of test databases for details. MIGRATE Default: True When set to False, migrations won’t run when creating the test database. This is similar to setting None as a value in MIGRATION_MODULES, but for all apps. MIRROR Default: None The alias of the database that this database should mirror during testing. This setting exists to allow for testing of primary/replica (referred to as master/slave by some databases) configurations of multiple databases. See the documentation on testing primary/replica configurations for details. NAME Default: None The name of database to use when running the test suite. If the default value (None) is used with the SQLite database engine, the tests will use a memory resident database. For all other database engines the test database will use the name 'test_' + DATABASE_NAME. See The test database. SERIALIZE Boolean value to control whether or not the default test runner serializes the database into an in-memory JSON string before running tests (used to restore the database state between tests if you don’t have transactions). You can set this to False to speed up creation time if you don’t have any test classes with serialized_rollback=True. Deprecated since version 4.0: This setting is deprecated as it can be inferred from the databases with the serialized_rollback option enabled. TEMPLATE This is a PostgreSQL-specific setting. The name of a template (e.g. 'template0') from which to create the test database. CREATE_DB Default: True This is an Oracle-specific setting. If it is set to False, the test tablespaces won’t be automatically created at the beginning of the tests or dropped at the end. CREATE_USER Default: True This is an Oracle-specific setting. If it is set to False, the test user won’t be automatically created at the beginning of the tests and dropped at the end. USER Default: None This is an Oracle-specific setting. The username to use when connecting to the Oracle database that will be used when running tests. If not provided, Django will use 'test_' + USER. PASSWORD Default: None This is an Oracle-specific setting. The password to use when connecting to the Oracle database that will be used when running tests. If not provided, Django will generate a random password. ORACLE_MANAGED_FILES Default: False This is an Oracle-specific setting. If set to True, Oracle Managed Files (OMF) tablespaces will be used. DATAFILE and DATAFILE_TMP will be ignored. TBLSPACE Default: None This is an Oracle-specific setting. The name of the tablespace that will be used when running tests. If not provided, Django will use 'test_' + USER. TBLSPACE_TMP Default: None This is an Oracle-specific setting. The name of the temporary tablespace that will be used when running tests. If not provided, Django will use 'test_' + USER + '_temp'. DATAFILE Default: None This is an Oracle-specific setting. The name of the datafile to use for the TBLSPACE. If not provided, Django will use TBLSPACE + '.dbf'. DATAFILE_TMP Default: None This is an Oracle-specific setting. The name of the datafile to use for the TBLSPACE_TMP. If not provided, Django will use TBLSPACE_TMP + '.dbf'. DATAFILE_MAXSIZE Default: '500M' This is an Oracle-specific setting. The maximum size that the DATAFILE is allowed to grow to. DATAFILE_TMP_MAXSIZE Default: '500M' This is an Oracle-specific setting. The maximum size that the DATAFILE_TMP is allowed to grow to. DATAFILE_SIZE Default: '50M' This is an Oracle-specific setting. The initial size of the DATAFILE. DATAFILE_TMP_SIZE Default: '50M' This is an Oracle-specific setting. The initial size of the DATAFILE_TMP. DATAFILE_EXTSIZE Default: '25M' This is an Oracle-specific setting. The amount by which the DATAFILE is extended when more space is required. DATAFILE_TMP_EXTSIZE Default: '25M' This is an Oracle-specific setting. The amount by which the DATAFILE_TMP is extended when more space is required. DATA_UPLOAD_MAX_MEMORY_SIZE Default: 2621440 (i.e. 2.5 MB). The maximum size in bytes that a request body may be before a SuspiciousOperation (RequestDataTooBig) is raised. The check is done when accessing request.body or request.POST and is calculated against the total request size excluding any file upload data. You can set this to None to disable the check. Applications that are expected to receive unusually large form posts should tune this setting. The amount of request data is correlated to the amount of memory needed to process the request and populate the GET and POST dictionaries. Large requests could be used as a denial-of-service attack vector if left unchecked. Since web servers don’t typically perform deep request inspection, it’s not possible to perform a similar check at that level. See also FILE_UPLOAD_MAX_MEMORY_SIZE. DATA_UPLOAD_MAX_NUMBER_FIELDS Default: 1000 The maximum number of parameters that may be received via GET or POST before a SuspiciousOperation (TooManyFields) is raised. You can set this to None to disable the check. Applications that are expected to receive an unusually large number of form fields should tune this setting. The number of request parameters is correlated to the amount of time needed to process the request and populate the GET and POST dictionaries. Large requests could be used as a denial-of-service attack vector if left unchecked. Since web servers don’t typically perform deep request inspection, it’s not possible to perform a similar check at that level. DATABASE_ROUTERS Default: [] (Empty list) The list of routers that will be used to determine which database to use when performing a database query. See the documentation on automatic database routing in multi database configurations. DATE_FORMAT Default: 'N j, Y' (e.g. Feb. 4, 2003) The default formatting to use for displaying date fields in any part of the system. Note that if USE_L10N is set to True, then the locale-dictated format has higher precedence and will be applied instead. See allowed date format strings. See also DATETIME_FORMAT, TIME_FORMAT and SHORT_DATE_FORMAT. DATE_INPUT_FORMATS Default: [
'%Y-%m-%d', '%m/%d/%Y', '%m/%d/%y', # '2006-10-25', '10/25/2006', '10/25/06'
'%b %d %Y', '%b %d, %Y', # 'Oct 25 2006', 'Oct 25, 2006'
'%d %b %Y', '%d %b, %Y', # '25 Oct 2006', '25 Oct, 2006'
'%B %d %Y', '%B %d, %Y', # 'October 25 2006', 'October 25, 2006'
'%d %B %Y', '%d %B, %Y', # '25 October 2006', '25 October, 2006'
]
A list of formats that will be accepted when inputting data on a date field. Formats will be tried in order, using the first valid one. Note that these format strings use Python’s datetime module syntax, not the format strings from the date template filter. When USE_L10N is True, the locale-dictated format has higher precedence and will be applied instead. See also DATETIME_INPUT_FORMATS and TIME_INPUT_FORMATS. DATETIME_FORMAT Default: 'N j, Y, P' (e.g. Feb. 4, 2003, 4 p.m.) The default formatting to use for displaying datetime fields in any part of the system. Note that if USE_L10N is set to True, then the locale-dictated format has higher precedence and will be applied instead. See allowed date format strings. See also DATE_FORMAT, TIME_FORMAT and SHORT_DATETIME_FORMAT. DATETIME_INPUT_FORMATS Default: [
'%Y-%m-%d %H:%M:%S', # '2006-10-25 14:30:59'
'%Y-%m-%d %H:%M:%S.%f', # '2006-10-25 14:30:59.000200'
'%Y-%m-%d %H:%M', # '2006-10-25 14:30'
'%m/%d/%Y %H:%M:%S', # '10/25/2006 14:30:59'
'%m/%d/%Y %H:%M:%S.%f', # '10/25/2006 14:30:59.000200'
'%m/%d/%Y %H:%M', # '10/25/2006 14:30'
'%m/%d/%y %H:%M:%S', # '10/25/06 14:30:59'
'%m/%d/%y %H:%M:%S.%f', # '10/25/06 14:30:59.000200'
'%m/%d/%y %H:%M', # '10/25/06 14:30'
]
A list of formats that will be accepted when inputting data on a datetime field. Formats will be tried in order, using the first valid one. Note that these format strings use Python’s datetime module syntax, not the format strings from the date template filter. Date-only formats are not included as datetime fields will automatically try DATE_INPUT_FORMATS in last resort. When USE_L10N is True, the locale-dictated format has higher precedence and will be applied instead. See also DATE_INPUT_FORMATS and TIME_INPUT_FORMATS. DEBUG Default: False A boolean that turns on/off debug mode. Never deploy a site into production with DEBUG turned on. One of the main features of debug mode is the display of detailed error pages. If your app raises an exception when DEBUG is True, Django will display a detailed traceback, including a lot of metadata about your environment, such as all the currently defined Django settings (from settings.py). As a security measure, Django will not include settings that might be sensitive, such as SECRET_KEY. Specifically, it will exclude any setting whose name includes any of the following: 'API' 'KEY' 'PASS' 'SECRET' 'SIGNATURE' 'TOKEN' Note that these are partial matches. 'PASS' will also match PASSWORD, just as 'TOKEN' will also match TOKENIZED and so on. Still, note that there are always going to be sections of your debug output that are inappropriate for public consumption. File paths, configuration options and the like all give attackers extra information about your server. It is also important to remember that when running with DEBUG turned on, Django will remember every SQL query it executes. This is useful when you’re debugging, but it’ll rapidly consume memory on a production server. Finally, if DEBUG is False, you also need to properly set the ALLOWED_HOSTS setting. Failing to do so will result in all requests being returned as “Bad Request (400)”. Note The default settings.py file created by django-admin
startproject sets DEBUG = True for convenience. DEBUG_PROPAGATE_EXCEPTIONS Default: False If set to True, Django’s exception handling of view functions (handler500, or the debug view if DEBUG is True) and logging of 500 responses (django.request) is skipped and exceptions propagate upward. This can be useful for some test setups. It shouldn’t be used on a live site unless you want your web server (instead of Django) to generate “Internal Server Error” responses. In that case, make sure your server doesn’t show the stack trace or other sensitive information in the response. DECIMAL_SEPARATOR Default: '.' (Dot) Default decimal separator used when formatting decimal numbers. Note that if USE_L10N is set to True, then the locale-dictated format has higher precedence and will be applied instead. See also NUMBER_GROUPING, THOUSAND_SEPARATOR and USE_THOUSAND_SEPARATOR. DEFAULT_AUTO_FIELD New in Django 3.2. Default: 'django.db.models.AutoField' Default primary key field type to use for models that don’t have a field with primary_key=True. Migrating auto-created through tables The value of DEFAULT_AUTO_FIELD will be respected when creating new auto-created through tables for many-to-many relationships. Unfortunately, the primary keys of existing auto-created through tables cannot currently be updated by the migrations framework. This means that if you switch the value of DEFAULT_AUTO_FIELD and then generate migrations, the primary keys of the related models will be updated, as will the foreign keys from the through table, but the primary key of the auto-created through table will not be migrated. In order to address this, you should add a RunSQL operation to your migrations to perform the required ALTER TABLE step. You can check the existing table name through sqlmigrate, dbshell, or with the field’s remote_field.through._meta.db_table property. Explicitly defined through models are already handled by the migrations system. Allowing automatic migrations for the primary key of existing auto-created through tables may be implemented at a later date. DEFAULT_CHARSET Default: 'utf-8' Default charset to use for all HttpResponse objects, if a MIME type isn’t manually specified. Used when constructing the Content-Type header. DEFAULT_EXCEPTION_REPORTER Default: 'django.views.debug.ExceptionReporter' Default exception reporter class to be used if none has been assigned to the HttpRequest instance yet. See Custom error reports. DEFAULT_EXCEPTION_REPORTER_FILTER Default: 'django.views.debug.SafeExceptionReporterFilter' Default exception reporter filter class to be used if none has been assigned to the HttpRequest instance yet. See Filtering error reports. DEFAULT_FILE_STORAGE Default: 'django.core.files.storage.FileSystemStorage' Default file storage class to be used for any file-related operations that don’t specify a particular storage system. See Managing files. DEFAULT_FROM_EMAIL Default: 'webmaster@localhost' Default email address to use for various automated correspondence from the site manager(s). This doesn’t include error messages sent to ADMINS and MANAGERS; for that, see SERVER_EMAIL. DEFAULT_INDEX_TABLESPACE Default: '' (Empty string) Default tablespace to use for indexes on fields that don’t specify one, if the backend supports it (see Tablespaces). DEFAULT_TABLESPACE Default: '' (Empty string) Default tablespace to use for models that don’t specify one, if the backend supports it (see Tablespaces). DISALLOWED_USER_AGENTS Default: [] (Empty list) List of compiled regular expression objects representing User-Agent strings that are not allowed to visit any page, systemwide. Use this for bots/crawlers. This is only used if CommonMiddleware is installed (see Middleware). EMAIL_BACKEND Default: 'django.core.mail.backends.smtp.EmailBackend' The backend to use for sending emails. For the list of available backends see Sending email. EMAIL_FILE_PATH Default: Not defined The directory used by the file email backend to store output files. EMAIL_HOST Default: 'localhost' The host to use for sending email. See also EMAIL_PORT. EMAIL_HOST_PASSWORD Default: '' (Empty string) Password to use for the SMTP server defined in EMAIL_HOST. This setting is used in conjunction with EMAIL_HOST_USER when authenticating to the SMTP server. If either of these settings is empty, Django won’t attempt authentication. See also EMAIL_HOST_USER. EMAIL_HOST_USER Default: '' (Empty string) Username to use for the SMTP server defined in EMAIL_HOST. If empty, Django won’t attempt authentication. See also EMAIL_HOST_PASSWORD. EMAIL_PORT Default: 25 Port to use for the SMTP server defined in EMAIL_HOST. EMAIL_SUBJECT_PREFIX Default: '[Django] ' Subject-line prefix for email messages sent with django.core.mail.mail_admins or django.core.mail.mail_managers. You’ll probably want to include the trailing space. EMAIL_USE_LOCALTIME Default: False Whether to send the SMTP Date header of email messages in the local time zone (True) or in UTC (False). EMAIL_USE_TLS Default: False Whether to use a TLS (secure) connection when talking to the SMTP server. This is used for explicit TLS connections, generally on port 587. If you are experiencing hanging connections, see the implicit TLS setting EMAIL_USE_SSL. EMAIL_USE_SSL Default: False Whether to use an implicit TLS (secure) connection when talking to the SMTP server. In most email documentation this type of TLS connection is referred to as SSL. It is generally used on port 465. If you are experiencing problems, see the explicit TLS setting EMAIL_USE_TLS. Note that EMAIL_USE_TLS/EMAIL_USE_SSL are mutually exclusive, so only set one of those settings to True. EMAIL_SSL_CERTFILE Default: None If EMAIL_USE_SSL or EMAIL_USE_TLS is True, you can optionally specify the path to a PEM-formatted certificate chain file to use for the SSL connection. EMAIL_SSL_KEYFILE Default: None If EMAIL_USE_SSL or EMAIL_USE_TLS is True, you can optionally specify the path to a PEM-formatted private key file to use for the SSL connection. Note that setting EMAIL_SSL_CERTFILE and EMAIL_SSL_KEYFILE doesn’t result in any certificate checking. They’re passed to the underlying SSL connection. Please refer to the documentation of Python’s ssl.wrap_socket() function for details on how the certificate chain file and private key file are handled. EMAIL_TIMEOUT Default: None Specifies a timeout in seconds for blocking operations like the connection attempt. FILE_UPLOAD_HANDLERS Default: [
'django.core.files.uploadhandler.MemoryFileUploadHandler',
'django.core.files.uploadhandler.TemporaryFileUploadHandler',
]
A list of handlers to use for uploading. Changing this setting allows complete customization – even replacement – of Django’s upload process. See Managing files for details. FILE_UPLOAD_MAX_MEMORY_SIZE Default: 2621440 (i.e. 2.5 MB). The maximum size (in bytes) that an upload will be before it gets streamed to the file system. See Managing files for details. See also DATA_UPLOAD_MAX_MEMORY_SIZE. FILE_UPLOAD_DIRECTORY_PERMISSIONS Default: None The numeric mode to apply to directories created in the process of uploading files. This setting also determines the default permissions for collected static directories when using the collectstatic management command. See collectstatic for details on overriding it. This value mirrors the functionality and caveats of the FILE_UPLOAD_PERMISSIONS setting. FILE_UPLOAD_PERMISSIONS Default: 0o644 The numeric mode (i.e. 0o644) to set newly uploaded files to. For more information about what these modes mean, see the documentation for os.chmod(). If None, you’ll get operating-system dependent behavior. On most platforms, temporary files will have a mode of 0o600, and files saved from memory will be saved using the system’s standard umask. For security reasons, these permissions aren’t applied to the temporary files that are stored in FILE_UPLOAD_TEMP_DIR. This setting also determines the default permissions for collected static files when using the collectstatic management command. See collectstatic for details on overriding it. Warning Always prefix the mode with 0o . If you’re not familiar with file modes, please note that the 0o prefix is very important: it indicates an octal number, which is the way that modes must be specified. If you try to use 644, you’ll get totally incorrect behavior. FILE_UPLOAD_TEMP_DIR Default: None The directory to store data to (typically files larger than FILE_UPLOAD_MAX_MEMORY_SIZE) temporarily while uploading files. If None, Django will use the standard temporary directory for the operating system. For example, this will default to /tmp on *nix-style operating systems. See Managing files for details. FIRST_DAY_OF_WEEK Default: 0 (Sunday) A number representing the first day of the week. This is especially useful when displaying a calendar. This value is only used when not using format internationalization, or when a format cannot be found for the current locale. The value must be an integer from 0 to 6, where 0 means Sunday, 1 means Monday and so on. FIXTURE_DIRS Default: [] (Empty list) List of directories searched for fixture files, in addition to the fixtures directory of each application, in search order. Note that these paths should use Unix-style forward slashes, even on Windows. See Providing data with fixtures and Fixture loading. FORCE_SCRIPT_NAME Default: None If not None, this will be used as the value of the SCRIPT_NAME environment variable in any HTTP request. This setting can be used to override the server-provided value of SCRIPT_NAME, which may be a rewritten version of the preferred value or not supplied at all. It is also used by django.setup() to set the URL resolver script prefix outside of the request/response cycle (e.g. in management commands and standalone scripts) to generate correct URLs when SCRIPT_NAME is not /. FORM_RENDERER Default: 'django.forms.renderers.DjangoTemplates' The class that renders forms and form widgets. It must implement the low-level render API. Included form renderers are:
'django.forms.renderers.DjangoTemplates'
'django.forms.renderers.Jinja2'
FORMAT_MODULE_PATH Default: None A full Python path to a Python package that contains custom format definitions for project locales. If not None, Django will check for a formats.py file, under the directory named as the current locale, and will use the formats defined in this file. For example, if FORMAT_MODULE_PATH is set to mysite.formats, and current language is en (English), Django will expect a directory tree like: mysite/
formats/
__init__.py
en/
__init__.py
formats.py
You can also set this setting to a list of Python paths, for example: FORMAT_MODULE_PATH = [
'mysite.formats',
'some_app.formats',
]
When Django searches for a certain format, it will go through all given Python paths until it finds a module that actually defines the given format. This means that formats defined in packages farther up in the list will take precedence over the same formats in packages farther down. Available formats are: DATE_FORMAT DATE_INPUT_FORMATS
DATETIME_FORMAT, DATETIME_INPUT_FORMATS DECIMAL_SEPARATOR FIRST_DAY_OF_WEEK MONTH_DAY_FORMAT NUMBER_GROUPING SHORT_DATE_FORMAT SHORT_DATETIME_FORMAT THOUSAND_SEPARATOR TIME_FORMAT TIME_INPUT_FORMATS YEAR_MONTH_FORMAT IGNORABLE_404_URLS Default: [] (Empty list) List of compiled regular expression objects describing URLs that should be ignored when reporting HTTP 404 errors via email (see How to manage error reporting). Regular expressions are matched against request's full paths (including query string, if any). Use this if your site does not provide a commonly requested file such as favicon.ico or robots.txt. This is only used if BrokenLinkEmailsMiddleware is enabled (see Middleware). INSTALLED_APPS Default: [] (Empty list) A list of strings designating all applications that are enabled in this Django installation. Each string should be a dotted Python path to: an application configuration class (preferred), or a package containing an application. Learn more about application configurations. Use the application registry for introspection Your code should never access INSTALLED_APPS directly. Use django.apps.apps instead. Application names and labels must be unique in INSTALLED_APPS Application names — the dotted Python path to the application package — must be unique. There is no way to include the same application twice, short of duplicating its code under another name. Application labels — by default the final part of the name — must be unique too. For example, you can’t include both django.contrib.auth and myproject.auth. However, you can relabel an application with a custom configuration that defines a different label. These rules apply regardless of whether INSTALLED_APPS references application configuration classes or application packages. When several applications provide different versions of the same resource (template, static file, management command, translation), the application listed first in INSTALLED_APPS has precedence. INTERNAL_IPS Default: [] (Empty list) A list of IP addresses, as strings, that: Allow the debug() context processor to add some variables to the template context. Can use the admindocs bookmarklets even if not logged in as a staff user. Are marked as “internal” (as opposed to “EXTERNAL”) in AdminEmailHandler emails. LANGUAGE_CODE Default: 'en-us' A string representing the language code for this installation. This should be in standard language ID format. For example, U.S. English is "en-us". See also the list of language identifiers and Internationalization and localization. USE_I18N must be active for this setting to have any effect. It serves two purposes: If the locale middleware isn’t in use, it decides which translation is served to all users. If the locale middleware is active, it provides a fallback language in case the user’s preferred language can’t be determined or is not supported by the website. It also provides the fallback translation when a translation for a given literal doesn’t exist for the user’s preferred language. See How Django discovers language preference for more details. LANGUAGE_COOKIE_AGE Default: None (expires at browser close) The age of the language cookie, in seconds. LANGUAGE_COOKIE_DOMAIN Default: None The domain to use for the language cookie. Set this to a string such as "example.com" for cross-domain cookies, or use None for a standard domain cookie. Be cautious when updating this setting on a production site. If you update this setting to enable cross-domain cookies on a site that previously used standard domain cookies, existing user cookies that have the old domain will not be updated. This will result in site users being unable to switch the language as long as these cookies persist. The only safe and reliable option to perform the switch is to change the language cookie name permanently (via the LANGUAGE_COOKIE_NAME setting) and to add a middleware that copies the value from the old cookie to a new one and then deletes the old one. LANGUAGE_COOKIE_HTTPONLY Default: False Whether to use HttpOnly flag on the language cookie. If this is set to True, client-side JavaScript will not be able to access the language cookie. See SESSION_COOKIE_HTTPONLY for details on HttpOnly. LANGUAGE_COOKIE_NAME Default: 'django_language' The name of the cookie to use for the language cookie. This can be whatever you want (as long as it’s different from the other cookie names in your application). See Internationalization and localization. LANGUAGE_COOKIE_PATH Default: '/' The path set on the language cookie. This should either match the URL path of your Django installation or be a parent of that path. This is useful if you have multiple Django instances running under the same hostname. They can use different cookie paths and each instance will only see its own language cookie. Be cautious when updating this setting on a production site. If you update this setting to use a deeper path than it previously used, existing user cookies that have the old path will not be updated. This will result in site users being unable to switch the language as long as these cookies persist. The only safe and reliable option to perform the switch is to change the language cookie name permanently (via the LANGUAGE_COOKIE_NAME setting), and to add a middleware that copies the value from the old cookie to a new one and then deletes the one. LANGUAGE_COOKIE_SAMESITE Default: None The value of the SameSite flag on the language cookie. This flag prevents the cookie from being sent in cross-site requests. See SESSION_COOKIE_SAMESITE for details about SameSite. LANGUAGE_COOKIE_SECURE Default: False Whether to use a secure cookie for the language cookie. If this is set to True, the cookie will be marked as “secure”, which means browsers may ensure that the cookie is only sent under an HTTPS connection. LANGUAGES Default: A list of all available languages. This list is continually growing and including a copy here would inevitably become rapidly out of date. You can see the current list of translated languages by looking in django/conf/global_settings.py. The list is a list of two-tuples in the format (language code, language name) – for example, ('ja', 'Japanese'). This specifies which languages are available for language selection. See Internationalization and localization. Generally, the default value should suffice. Only set this setting if you want to restrict language selection to a subset of the Django-provided languages. If you define a custom LANGUAGES setting, you can mark the language names as translation strings using the gettext_lazy() function. Here’s a sample settings file: from django.utils.translation import gettext_lazy as _
LANGUAGES = [
('de', _('German')),
('en', _('English')),
]
LANGUAGES_BIDI Default: A list of all language codes that are written right-to-left. You can see the current list of these languages by looking in django/conf/global_settings.py. The list contains language codes for languages that are written right-to-left. Generally, the default value should suffice. Only set this setting if you want to restrict language selection to a subset of the Django-provided languages. If you define a custom LANGUAGES setting, the list of bidirectional languages may contain language codes which are not enabled on a given site. LOCALE_PATHS Default: [] (Empty list) A list of directories where Django looks for translation files. See How Django discovers translations. Example: LOCALE_PATHS = [
'/home/www/project/common_files/locale',
'/var/local/translations/locale',
]
Django will look within each of these paths for the <locale_code>/LC_MESSAGES directories containing the actual translation files. LOGGING Default: A logging configuration dictionary. A data structure containing configuration information. The contents of this data structure will be passed as the argument to the configuration method described in LOGGING_CONFIG. Among other things, the default logging configuration passes HTTP 500 server errors to an email log handler when DEBUG is False. See also Configuring logging. You can see the default logging configuration by looking in django/utils/log.py. LOGGING_CONFIG Default: 'logging.config.dictConfig' A path to a callable that will be used to configure logging in the Django project. Points at an instance of Python’s dictConfig configuration method by default. If you set LOGGING_CONFIG to None, the logging configuration process will be skipped. MANAGERS Default: [] (Empty list) A list in the same format as ADMINS that specifies who should get broken link notifications when BrokenLinkEmailsMiddleware is enabled. MEDIA_ROOT Default: '' (Empty string) Absolute filesystem path to the directory that will hold user-uploaded files. Example: "/var/www/example.com/media/" See also MEDIA_URL. Warning MEDIA_ROOT and STATIC_ROOT must have different values. Before STATIC_ROOT was introduced, it was common to rely or fallback on MEDIA_ROOT to also serve static files; however, since this can have serious security implications, there is a validation check to prevent it. MEDIA_URL Default: '' (Empty string) URL that handles the media served from MEDIA_ROOT, used for managing stored files. It must end in a slash if set to a non-empty value. You will need to configure these files to be served in both development and production environments. If you want to use {{ MEDIA_URL }} in your templates, add 'django.template.context_processors.media' in the 'context_processors' option of TEMPLATES. Example: "http://media.example.com/" Warning There are security risks if you are accepting uploaded content from untrusted users! See the security guide’s topic on User-uploaded content for mitigation details. Warning MEDIA_URL and STATIC_URL must have different values. See MEDIA_ROOT for more details. Note If MEDIA_URL is a relative path, then it will be prefixed by the server-provided value of SCRIPT_NAME (or / if not set). This makes it easier to serve a Django application in a subpath without adding an extra configuration to the settings. MIDDLEWARE Default: None A list of middleware to use. See Middleware. MIGRATION_MODULES Default: {} (Empty dictionary) A dictionary specifying the package where migration modules can be found on a per-app basis. The default value of this setting is an empty dictionary, but the default package name for migration modules is migrations. Example: {'blog': 'blog.db_migrations'}
In this case, migrations pertaining to the blog app will be contained in the blog.db_migrations package. If you provide the app_label argument, makemigrations will automatically create the package if it doesn’t already exist. When you supply None as a value for an app, Django will consider the app as an app without migrations regardless of an existing migrations submodule. This can be used, for example, in a test settings file to skip migrations while testing (tables will still be created for the apps’ models). To disable migrations for all apps during tests, you can set the MIGRATE to False instead. If MIGRATION_MODULES is used in your general project settings, remember to use the migrate --run-syncdb option if you want to create tables for the app. MONTH_DAY_FORMAT Default: 'F j' The default formatting to use for date fields on Django admin change-list pages – and, possibly, by other parts of the system – in cases when only the month and day are displayed. For example, when a Django admin change-list page is being filtered by a date drilldown, the header for a given day displays the day and month. Different locales have different formats. For example, U.S. English would say “January 1,” whereas Spanish might say “1 Enero.” Note that if USE_L10N is set to True, then the corresponding locale-dictated format has higher precedence and will be applied. See allowed date format strings. See also DATE_FORMAT, DATETIME_FORMAT, TIME_FORMAT and YEAR_MONTH_FORMAT. NUMBER_GROUPING Default: 0 Number of digits grouped together on the integer part of a number. Common use is to display a thousand separator. If this setting is 0, then no grouping will be applied to the number. If this setting is greater than 0, then THOUSAND_SEPARATOR will be used as the separator between those groups. Some locales use non-uniform digit grouping, e.g. 10,00,00,000 in en_IN. For this case, you can provide a sequence with the number of digit group sizes to be applied. The first number defines the size of the group preceding the decimal delimiter, and each number that follows defines the size of preceding groups. If the sequence is terminated with -1, no further grouping is performed. If the sequence terminates with a 0, the last group size is used for the remainder of the number. Example tuple for en_IN: NUMBER_GROUPING = (3, 2, 0)
Note that if USE_L10N is set to True, then the locale-dictated format has higher precedence and will be applied instead. See also DECIMAL_SEPARATOR, THOUSAND_SEPARATOR and USE_THOUSAND_SEPARATOR. PREPEND_WWW Default: False Whether to prepend the “www.” subdomain to URLs that don’t have it. This is only used if CommonMiddleware is installed (see Middleware). See also APPEND_SLASH. ROOT_URLCONF Default: Not defined A string representing the full Python import path to your root URLconf, for example "mydjangoapps.urls". Can be overridden on a per-request basis by setting the attribute urlconf on the incoming HttpRequest object. See How Django processes a request for details. SECRET_KEY Default: '' (Empty string) A secret key for a particular Django installation. This is used to provide cryptographic signing, and should be set to a unique, unpredictable value. django-admin startproject automatically adds a randomly-generated SECRET_KEY to each new project. Uses of the key shouldn’t assume that it’s text or bytes. Every use should go through force_str() or force_bytes() to convert it to the desired type. Django will refuse to start if SECRET_KEY is not set. Warning Keep this value secret. Running Django with a known SECRET_KEY defeats many of Django’s security protections, and can lead to privilege escalation and remote code execution vulnerabilities. The secret key is used for: All sessions if you are using any other session backend than django.contrib.sessions.backends.cache, or are using the default get_session_auth_hash(). All messages if you are using CookieStorage or FallbackStorage. All PasswordResetView tokens. Any usage of cryptographic signing, unless a different key is provided. If you rotate your secret key, all of the above will be invalidated. Secret keys are not used for passwords of users and key rotation will not affect them. Note The default settings.py file created by django-admin
startproject creates a unique SECRET_KEY for convenience. SECURE_CONTENT_TYPE_NOSNIFF Default: True If True, the SecurityMiddleware sets the X-Content-Type-Options: nosniff header on all responses that do not already have it. SECURE_CROSS_ORIGIN_OPENER_POLICY New in Django 4.0. Default: 'same-origin' Unless set to None, the SecurityMiddleware sets the Cross-Origin Opener Policy header on all responses that do not already have it to the value provided. SECURE_HSTS_INCLUDE_SUBDOMAINS Default: False If True, the SecurityMiddleware adds the includeSubDomains directive to the HTTP Strict Transport Security header. It has no effect unless SECURE_HSTS_SECONDS is set to a non-zero value. Warning Setting this incorrectly can irreversibly (for the value of SECURE_HSTS_SECONDS) break your site. Read the HTTP Strict Transport Security documentation first. SECURE_HSTS_PRELOAD Default: False If True, the SecurityMiddleware adds the preload directive to the HTTP Strict Transport Security header. It has no effect unless SECURE_HSTS_SECONDS is set to a non-zero value. SECURE_HSTS_SECONDS Default: 0 If set to a non-zero integer value, the SecurityMiddleware sets the HTTP Strict Transport Security header on all responses that do not already have it. Warning Setting this incorrectly can irreversibly (for some time) break your site. Read the HTTP Strict Transport Security documentation first. SECURE_PROXY_SSL_HEADER Default: None A tuple representing an HTTP header/value combination that signifies a request is secure. This controls the behavior of the request object’s is_secure() method. By default, is_secure() determines if a request is secure by confirming that a requested URL uses https://. This method is important for Django’s CSRF protection, and it may be used by your own code or third-party apps. If your Django app is behind a proxy, though, the proxy may be “swallowing” whether the original request uses HTTPS or not. If there is a non-HTTPS connection between the proxy and Django then is_secure() would always return False – even for requests that were made via HTTPS by the end user. In contrast, if there is an HTTPS connection between the proxy and Django then is_secure() would always return True – even for requests that were made originally via HTTP. In this situation, configure your proxy to set a custom HTTP header that tells Django whether the request came in via HTTPS, and set SECURE_PROXY_SSL_HEADER so that Django knows what header to look for. Set a tuple with two elements – the name of the header to look for and the required value. For example: SECURE_PROXY_SSL_HEADER = ('HTTP_X_FORWARDED_PROTO', 'https')
This tells Django to trust the X-Forwarded-Proto header that comes from our proxy, and any time its value is 'https', then the request is guaranteed to be secure (i.e., it originally came in via HTTPS). You should only set this setting if you control your proxy or have some other guarantee that it sets/strips this header appropriately. Note that the header needs to be in the format as used by request.META – all caps and likely starting with HTTP_. (Remember, Django automatically adds 'HTTP_' to the start of x-header names before making the header available in request.META.) Warning Modifying this setting can compromise your site’s security. Ensure you fully understand your setup before changing it. Make sure ALL of the following are true before setting this (assuming the values from the example above): Your Django app is behind a proxy. Your proxy strips the X-Forwarded-Proto header from all incoming requests. In other words, if end users include that header in their requests, the proxy will discard it. Your proxy sets the X-Forwarded-Proto header and sends it to Django, but only for requests that originally come in via HTTPS. If any of those are not true, you should keep this setting set to None and find another way of determining HTTPS, perhaps via custom middleware. SECURE_REDIRECT_EXEMPT Default: [] (Empty list) If a URL path matches a regular expression in this list, the request will not be redirected to HTTPS. The SecurityMiddleware strips leading slashes from URL paths, so patterns shouldn’t include them, e.g. SECURE_REDIRECT_EXEMPT = [r'^no-ssl/$', …]. If SECURE_SSL_REDIRECT is False, this setting has no effect. SECURE_REFERRER_POLICY Default: 'same-origin' If configured, the SecurityMiddleware sets the Referrer Policy header on all responses that do not already have it to the value provided. SECURE_SSL_HOST Default: None If a string (e.g. secure.example.com), all SSL redirects will be directed to this host rather than the originally-requested host (e.g. www.example.com). If SECURE_SSL_REDIRECT is False, this setting has no effect. SECURE_SSL_REDIRECT Default: False If True, the SecurityMiddleware redirects all non-HTTPS requests to HTTPS (except for those URLs matching a regular expression listed in SECURE_REDIRECT_EXEMPT). Note If turning this to True causes infinite redirects, it probably means your site is running behind a proxy and can’t tell which requests are secure and which are not. Your proxy likely sets a header to indicate secure requests; you can correct the problem by finding out what that header is and configuring the SECURE_PROXY_SSL_HEADER setting accordingly. SERIALIZATION_MODULES Default: Not defined A dictionary of modules containing serializer definitions (provided as strings), keyed by a string identifier for that serialization type. For example, to define a YAML serializer, use: SERIALIZATION_MODULES = {'yaml': 'path.to.yaml_serializer'}
SERVER_EMAIL Default: 'root@localhost' The email address that error messages come from, such as those sent to ADMINS and MANAGERS. Why are my emails sent from a different address? This address is used only for error messages. It is not the address that regular email messages sent with send_mail() come from; for that, see DEFAULT_FROM_EMAIL. SHORT_DATE_FORMAT Default: 'm/d/Y' (e.g. 12/31/2003) An available formatting that can be used for displaying date fields on templates. Note that if USE_L10N is set to True, then the corresponding locale-dictated format has higher precedence and will be applied. See allowed date format strings. See also DATE_FORMAT and SHORT_DATETIME_FORMAT. SHORT_DATETIME_FORMAT Default: 'm/d/Y P' (e.g. 12/31/2003 4 p.m.) An available formatting that can be used for displaying datetime fields on templates. Note that if USE_L10N is set to True, then the corresponding locale-dictated format has higher precedence and will be applied. See allowed date format strings. See also DATE_FORMAT and SHORT_DATE_FORMAT. SIGNING_BACKEND Default: 'django.core.signing.TimestampSigner' The backend used for signing cookies and other data. See also the Cryptographic signing documentation. SILENCED_SYSTEM_CHECKS Default: [] (Empty list) A list of identifiers of messages generated by the system check framework (i.e. ["models.W001"]) that you wish to permanently acknowledge and ignore. Silenced checks will not be output to the console. See also the System check framework documentation. TEMPLATES Default: [] (Empty list) A list containing the settings for all template engines to be used with Django. Each item of the list is a dictionary containing the options for an individual engine. Here’s a setup that tells the Django template engine to load templates from the templates subdirectory inside each installed application: TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'APP_DIRS': True,
},
]
The following options are available for all backends. BACKEND Default: Not defined The template backend to use. The built-in template backends are: 'django.template.backends.django.DjangoTemplates' 'django.template.backends.jinja2.Jinja2' You can use a template backend that doesn’t ship with Django by setting BACKEND to a fully-qualified path (i.e. 'mypackage.whatever.Backend'). NAME Default: see below The alias for this particular template engine. It’s an identifier that allows selecting an engine for rendering. Aliases must be unique across all configured template engines. It defaults to the name of the module defining the engine class, i.e. the next to last piece of BACKEND, when it isn’t provided. For example if the backend is 'mypackage.whatever.Backend' then its default name is 'whatever'. DIRS Default: [] (Empty list) Directories where the engine should look for template source files, in search order. APP_DIRS Default: False Whether the engine should look for template source files inside installed applications. Note The default settings.py file created by django-admin
startproject sets 'APP_DIRS': True. OPTIONS Default: {} (Empty dict) Extra parameters to pass to the template backend. Available parameters vary depending on the template backend. See DjangoTemplates and Jinja2 for the options of the built-in backends. TEST_RUNNER Default: 'django.test.runner.DiscoverRunner' The name of the class to use for starting the test suite. See Using different testing frameworks. TEST_NON_SERIALIZED_APPS Default: [] (Empty list) In order to restore the database state between tests for TransactionTestCases and database backends without transactions, Django will serialize the contents of all apps when it starts the test run so it can then reload from that copy before running tests that need it. This slows down the startup time of the test runner; if you have apps that you know don’t need this feature, you can add their full names in here (e.g. 'django.contrib.contenttypes') to exclude them from this serialization process. THOUSAND_SEPARATOR Default: ',' (Comma) Default thousand separator used when formatting numbers. This setting is used only when USE_THOUSAND_SEPARATOR is True and NUMBER_GROUPING is greater than 0. Note that if USE_L10N is set to True, then the locale-dictated format has higher precedence and will be applied instead. See also NUMBER_GROUPING, DECIMAL_SEPARATOR and USE_THOUSAND_SEPARATOR. TIME_FORMAT Default: 'P' (e.g. 4 p.m.) The default formatting to use for displaying time fields in any part of the system. Note that if USE_L10N is set to True, then the locale-dictated format has higher precedence and will be applied instead. See allowed date format strings. See also DATE_FORMAT and DATETIME_FORMAT. TIME_INPUT_FORMATS Default: [
'%H:%M:%S', # '14:30:59'
'%H:%M:%S.%f', # '14:30:59.000200'
'%H:%M', # '14:30'
]
A list of formats that will be accepted when inputting data on a time field. Formats will be tried in order, using the first valid one. Note that these format strings use Python’s datetime module syntax, not the format strings from the date template filter. When USE_L10N is True, the locale-dictated format has higher precedence and will be applied instead. See also DATE_INPUT_FORMATS and DATETIME_INPUT_FORMATS. TIME_ZONE Default: 'America/Chicago' A string representing the time zone for this installation. See the list of time zones. Note Since Django was first released with the TIME_ZONE set to 'America/Chicago', the global setting (used if nothing is defined in your project’s settings.py) remains 'America/Chicago' for backwards compatibility. New project templates default to 'UTC'. Note that this isn’t necessarily the time zone of the server. For example, one server may serve multiple Django-powered sites, each with a separate time zone setting. When USE_TZ is False, this is the time zone in which Django will store all datetimes. When USE_TZ is True, this is the default time zone that Django will use to display datetimes in templates and to interpret datetimes entered in forms. On Unix environments (where time.tzset() is implemented), Django sets the os.environ['TZ'] variable to the time zone you specify in the TIME_ZONE setting. Thus, all your views and models will automatically operate in this time zone. However, Django won’t set the TZ environment variable if you’re using the manual configuration option as described in manually configuring settings. If Django doesn’t set the TZ environment variable, it’s up to you to ensure your processes are running in the correct environment. Note Django cannot reliably use alternate time zones in a Windows environment. If you’re running Django on Windows, TIME_ZONE must be set to match the system time zone. USE_DEPRECATED_PYTZ New in Django 4.0. Default: False A boolean that specifies whether to use pytz, rather than zoneinfo, as the default time zone implementation. Deprecated since version 4.0: This transitional setting is deprecated. Support for using pytz will be removed in Django 5.0. USE_I18N Default: True A boolean that specifies whether Django’s translation system should be enabled. This provides a way to turn it off, for performance. If this is set to False, Django will make some optimizations so as not to load the translation machinery. See also LANGUAGE_CODE, USE_L10N and USE_TZ. Note The default settings.py file created by django-admin
startproject includes USE_I18N = True for convenience. USE_L10N Default: True A boolean that specifies if localized formatting of data will be enabled by default or not. If this is set to True, e.g. Django will display numbers and dates using the format of the current locale. See also LANGUAGE_CODE, USE_I18N and USE_TZ. Changed in Django 4.0: In older versions, the default value is False. Deprecated since version 4.0: This setting is deprecated. Starting with Django 5.0, localized formatting of data will always be enabled. For example Django will display numbers and dates using the format of the current locale. USE_THOUSAND_SEPARATOR Default: False A boolean that specifies whether to display numbers using a thousand separator. When set to True and USE_L10N is also True, Django will format numbers using the NUMBER_GROUPING and THOUSAND_SEPARATOR settings. These settings may also be dictated by the locale, which takes precedence. See also DECIMAL_SEPARATOR, NUMBER_GROUPING and THOUSAND_SEPARATOR. USE_TZ Default: False Note In Django 5.0, the default value will change from False to True. A boolean that specifies if datetimes will be timezone-aware by default or not. If this is set to True, Django will use timezone-aware datetimes internally. When USE_TZ is False, Django will use naive datetimes in local time, except when parsing ISO 8601 formatted strings, where timezone information will always be retained if present. See also TIME_ZONE, USE_I18N and USE_L10N. Note The default settings.py file created by django-admin startproject includes USE_TZ = True for convenience. USE_X_FORWARDED_HOST Default: False A boolean that specifies whether to use the X-Forwarded-Host header in preference to the Host header. This should only be enabled if a proxy which sets this header is in use. This setting takes priority over USE_X_FORWARDED_PORT. Per RFC 7239#section-5.3, the X-Forwarded-Host header can include the port number, in which case you shouldn’t use USE_X_FORWARDED_PORT. USE_X_FORWARDED_PORT Default: False A boolean that specifies whether to use the X-Forwarded-Port header in preference to the SERVER_PORT META variable. This should only be enabled if a proxy which sets this header is in use. USE_X_FORWARDED_HOST takes priority over this setting. WSGI_APPLICATION Default: None The full Python path of the WSGI application object that Django’s built-in servers (e.g. runserver) will use. The django-admin
startproject management command will create a standard wsgi.py file with an application callable in it, and point this setting to that application. If not set, the return value of django.core.wsgi.get_wsgi_application() will be used. In this case, the behavior of runserver will be identical to previous Django versions. YEAR_MONTH_FORMAT Default: 'F Y' The default formatting to use for date fields on Django admin change-list pages – and, possibly, by other parts of the system – in cases when only the year and month are displayed. For example, when a Django admin change-list page is being filtered by a date drilldown, the header for a given month displays the month and the year. Different locales have different formats. For example, U.S. English would say “January 2006,” whereas another locale might say “2006/January.” Note that if USE_L10N is set to True, then the corresponding locale-dictated format has higher precedence and will be applied. See allowed date format strings. See also DATE_FORMAT, DATETIME_FORMAT, TIME_FORMAT and MONTH_DAY_FORMAT. X_FRAME_OPTIONS Default: 'DENY' The default value for the X-Frame-Options header used by XFrameOptionsMiddleware. See the clickjacking protection documentation. Auth Settings for django.contrib.auth. AUTHENTICATION_BACKENDS Default: ['django.contrib.auth.backends.ModelBackend'] A list of authentication backend classes (as strings) to use when attempting to authenticate a user. See the authentication backends documentation for details. AUTH_USER_MODEL Default: 'auth.User' The model to use to represent a User. See Substituting a custom User model. Warning You cannot change the AUTH_USER_MODEL setting during the lifetime of a project (i.e. once you have made and migrated models that depend on it) without serious effort. It is intended to be set at the project start, and the model it refers to must be available in the first migration of the app that it lives in. See Substituting a custom User model for more details. LOGIN_REDIRECT_URL Default: '/accounts/profile/' The URL or named URL pattern where requests are redirected after login when the LoginView doesn’t get a next GET parameter. LOGIN_URL Default: '/accounts/login/' The URL or named URL pattern where requests are redirected for login when using the login_required() decorator, LoginRequiredMixin, or AccessMixin. LOGOUT_REDIRECT_URL Default: None The URL or named URL pattern where requests are redirected after logout if LogoutView doesn’t have a next_page attribute. If None, no redirect will be performed and the logout view will be rendered. PASSWORD_RESET_TIMEOUT Default: 259200 (3 days, in seconds) The number of seconds a password reset link is valid for. Used by the PasswordResetConfirmView. Note Reducing the value of this timeout doesn’t make any difference to the ability of an attacker to brute-force a password reset token. Tokens are designed to be safe from brute-forcing without any timeout. This timeout exists to protect against some unlikely attack scenarios, such as someone gaining access to email archives that may contain old, unused password reset tokens. PASSWORD_HASHERS See How Django stores passwords. Default: [
'django.contrib.auth.hashers.PBKDF2PasswordHasher',
'django.contrib.auth.hashers.PBKDF2SHA1PasswordHasher',
'django.contrib.auth.hashers.Argon2PasswordHasher',
'django.contrib.auth.hashers.BCryptSHA256PasswordHasher',
]
AUTH_PASSWORD_VALIDATORS Default: [] (Empty list) The list of validators that are used to check the strength of user’s passwords. See Password validation for more details. By default, no validation is performed and all passwords are accepted. Messages Settings for django.contrib.messages. MESSAGE_LEVEL Default: messages.INFO Sets the minimum message level that will be recorded by the messages framework. See message levels for more details. Important If you override MESSAGE_LEVEL in your settings file and rely on any of the built-in constants, you must import the constants module directly to avoid the potential for circular imports, e.g.: from django.contrib.messages import constants as message_constants
MESSAGE_LEVEL = message_constants.DEBUG
If desired, you may specify the numeric values for the constants directly according to the values in the above constants table. MESSAGE_STORAGE Default: 'django.contrib.messages.storage.fallback.FallbackStorage' Controls where Django stores message data. Valid values are: 'django.contrib.messages.storage.fallback.FallbackStorage' 'django.contrib.messages.storage.session.SessionStorage' 'django.contrib.messages.storage.cookie.CookieStorage' See message storage backends for more details. The backends that use cookies – CookieStorage and FallbackStorage – use the value of SESSION_COOKIE_DOMAIN, SESSION_COOKIE_SECURE and SESSION_COOKIE_HTTPONLY when setting their cookies. MESSAGE_TAGS Default: {
messages.DEBUG: 'debug',
messages.INFO: 'info',
messages.SUCCESS: 'success',
messages.WARNING: 'warning',
messages.ERROR: 'error',
}
This sets the mapping of message level to message tag, which is typically rendered as a CSS class in HTML. If you specify a value, it will extend the default. This means you only have to specify those values which you need to override. See Displaying messages above for more details. Important If you override MESSAGE_TAGS in your settings file and rely on any of the built-in constants, you must import the constants module directly to avoid the potential for circular imports, e.g.: from django.contrib.messages import constants as message_constants
MESSAGE_TAGS = {message_constants.INFO: ''}
If desired, you may specify the numeric values for the constants directly according to the values in the above constants table. Sessions Settings for django.contrib.sessions. SESSION_CACHE_ALIAS Default: 'default' If you’re using cache-based session storage, this selects the cache to use. SESSION_COOKIE_AGE Default: 1209600 (2 weeks, in seconds) The age of session cookies, in seconds. SESSION_COOKIE_DOMAIN Default: None The domain to use for session cookies. Set this to a string such as "example.com" for cross-domain cookies, or use None for a standard domain cookie. To use cross-domain cookies with CSRF_USE_SESSIONS, you must include a leading dot (e.g. ".example.com") to accommodate the CSRF middleware’s referer checking. Be cautious when updating this setting on a production site. If you update this setting to enable cross-domain cookies on a site that previously used standard domain cookies, existing user cookies will be set to the old domain. This may result in them being unable to log in as long as these cookies persist. This setting also affects cookies set by django.contrib.messages. SESSION_COOKIE_HTTPONLY Default: True Whether to use HttpOnly flag on the session cookie. If this is set to True, client-side JavaScript will not be able to access the session cookie. HttpOnly is a flag included in a Set-Cookie HTTP response header. It’s part of the RFC 6265#section-4.1.2.6 standard for cookies and can be a useful way to mitigate the risk of a client-side script accessing the protected cookie data. This makes it less trivial for an attacker to escalate a cross-site scripting vulnerability into full hijacking of a user’s session. There aren’t many good reasons for turning this off. Your code shouldn’t read session cookies from JavaScript. SESSION_COOKIE_NAME Default: 'sessionid' The name of the cookie to use for sessions. This can be whatever you want (as long as it’s different from the other cookie names in your application). SESSION_COOKIE_PATH Default: '/' The path set on the session cookie. This should either match the URL path of your Django installation or be parent of that path. This is useful if you have multiple Django instances running under the same hostname. They can use different cookie paths, and each instance will only see its own session cookie. SESSION_COOKIE_SAMESITE Default: 'Lax' The value of the SameSite flag on the session cookie. This flag prevents the cookie from being sent in cross-site requests thus preventing CSRF attacks and making some methods of stealing session cookie impossible. Possible values for the setting are:
'Strict': prevents the cookie from being sent by the browser to the target site in all cross-site browsing context, even when following a regular link. For example, for a GitHub-like website this would mean that if a logged-in user follows a link to a private GitHub project posted on a corporate discussion forum or email, GitHub will not receive the session cookie and the user won’t be able to access the project. A bank website, however, most likely doesn’t want to allow any transactional pages to be linked from external sites so the 'Strict' flag would be appropriate.
'Lax' (default): provides a balance between security and usability for websites that want to maintain user’s logged-in session after the user arrives from an external link. In the GitHub scenario, the session cookie would be allowed when following a regular link from an external website and be blocked in CSRF-prone request methods (e.g. POST).
'None' (string): the session cookie will be sent with all same-site and cross-site requests.
False: disables the flag. Note Modern browsers provide a more secure default policy for the SameSite flag and will assume Lax for cookies without an explicit value set. SESSION_COOKIE_SECURE Default: False Whether to use a secure cookie for the session cookie. If this is set to True, the cookie will be marked as “secure”, which means browsers may ensure that the cookie is only sent under an HTTPS connection. Leaving this setting off isn’t a good idea because an attacker could capture an unencrypted session cookie with a packet sniffer and use the cookie to hijack the user’s session. SESSION_ENGINE Default: 'django.contrib.sessions.backends.db' Controls where Django stores session data. Included engines are: 'django.contrib.sessions.backends.db' 'django.contrib.sessions.backends.file' 'django.contrib.sessions.backends.cache' 'django.contrib.sessions.backends.cached_db' 'django.contrib.sessions.backends.signed_cookies' See Configuring the session engine for more details. SESSION_EXPIRE_AT_BROWSER_CLOSE Default: False Whether to expire the session when the user closes their browser. See Browser-length sessions vs. persistent sessions. SESSION_FILE_PATH Default: None If you’re using file-based session storage, this sets the directory in which Django will store session data. When the default value (None) is used, Django will use the standard temporary directory for the system. SESSION_SAVE_EVERY_REQUEST Default: False Whether to save the session data on every request. If this is False (default), then the session data will only be saved if it has been modified – that is, if any of its dictionary values have been assigned or deleted. Empty sessions won’t be created, even if this setting is active. SESSION_SERIALIZER Default: 'django.contrib.sessions.serializers.JSONSerializer' Full import path of a serializer class to use for serializing session data. Included serializers are: 'django.contrib.sessions.serializers.PickleSerializer' 'django.contrib.sessions.serializers.JSONSerializer' See Session serialization for details, including a warning regarding possible remote code execution when using PickleSerializer. Sites Settings for django.contrib.sites. SITE_ID Default: Not defined The ID, as an integer, of the current site in the django_site database table. This is used so that application data can hook into specific sites and a single database can manage content for multiple sites. Static Files Settings for django.contrib.staticfiles. STATIC_ROOT Default: None The absolute path to the directory where collectstatic will collect static files for deployment. Example: "/var/www/example.com/static/" If the staticfiles contrib app is enabled (as in the default project template), the collectstatic management command will collect static files into this directory. See the how-to on managing static files for more details about usage. Warning This should be an initially empty destination directory for collecting your static files from their permanent locations into one directory for ease of deployment; it is not a place to store your static files permanently. You should do that in directories that will be found by staticfiles’s finders, which by default, are 'static/' app sub-directories and any directories you include in STATICFILES_DIRS). STATIC_URL Default: None URL to use when referring to static files located in STATIC_ROOT. Example: "static/" or "http://static.example.com/" If not None, this will be used as the base path for asset definitions (the Media class) and the staticfiles app. It must end in a slash if set to a non-empty value. You may need to configure these files to be served in development and will definitely need to do so in production. Note If STATIC_URL is a relative path, then it will be prefixed by the server-provided value of SCRIPT_NAME (or / if not set). This makes it easier to serve a Django application in a subpath without adding an extra configuration to the settings. STATICFILES_DIRS Default: [] (Empty list) This setting defines the additional locations the staticfiles app will traverse if the FileSystemFinder finder is enabled, e.g. if you use the collectstatic or findstatic management command or use the static file serving view. This should be set to a list of strings that contain full paths to your additional files directory(ies) e.g.: STATICFILES_DIRS = [
"/home/special.polls.com/polls/static",
"/home/polls.com/polls/static",
"/opt/webfiles/common",
]
Note that these paths should use Unix-style forward slashes, even on Windows (e.g. "C:/Users/user/mysite/extra_static_content"). Prefixes (optional) In case you want to refer to files in one of the locations with an additional namespace, you can optionally provide a prefix as (prefix, path) tuples, e.g.: STATICFILES_DIRS = [
# ...
("downloads", "/opt/webfiles/stats"),
]
For example, assuming you have STATIC_URL set to 'static/', the collectstatic management command would collect the “stats” files in a 'downloads' subdirectory of STATIC_ROOT. This would allow you to refer to the local file '/opt/webfiles/stats/polls_20101022.tar.gz' with '/static/downloads/polls_20101022.tar.gz' in your templates, e.g.: <a href="{% static 'downloads/polls_20101022.tar.gz' %}">
STATICFILES_STORAGE Default: 'django.contrib.staticfiles.storage.StaticFilesStorage' The file storage engine to use when collecting static files with the collectstatic management command. A ready-to-use instance of the storage backend defined in this setting can be found at django.contrib.staticfiles.storage.staticfiles_storage. For an example, see Serving static files from a cloud service or CDN. STATICFILES_FINDERS Default: [
'django.contrib.staticfiles.finders.FileSystemFinder',
'django.contrib.staticfiles.finders.AppDirectoriesFinder',
]
The list of finder backends that know how to find static files in various locations. The default will find files stored in the STATICFILES_DIRS setting (using django.contrib.staticfiles.finders.FileSystemFinder) and in a static subdirectory of each app (using django.contrib.staticfiles.finders.AppDirectoriesFinder). If multiple files with the same name are present, the first file that is found will be used. One finder is disabled by default: django.contrib.staticfiles.finders.DefaultStorageFinder. If added to your STATICFILES_FINDERS setting, it will look for static files in the default file storage as defined by the DEFAULT_FILE_STORAGE setting. Note When using the AppDirectoriesFinder finder, make sure your apps can be found by staticfiles by adding the app to the INSTALLED_APPS setting of your site. Static file finders are currently considered a private interface, and this interface is thus undocumented. Core Settings Topical Index Cache CACHES CACHE_MIDDLEWARE_ALIAS CACHE_MIDDLEWARE_KEY_PREFIX CACHE_MIDDLEWARE_SECONDS Database DATABASES DATABASE_ROUTERS DEFAULT_INDEX_TABLESPACE DEFAULT_TABLESPACE Debugging DEBUG DEBUG_PROPAGATE_EXCEPTIONS Email ADMINS DEFAULT_CHARSET DEFAULT_FROM_EMAIL EMAIL_BACKEND EMAIL_FILE_PATH EMAIL_HOST EMAIL_HOST_PASSWORD EMAIL_HOST_USER EMAIL_PORT EMAIL_SSL_CERTFILE EMAIL_SSL_KEYFILE EMAIL_SUBJECT_PREFIX EMAIL_TIMEOUT EMAIL_USE_LOCALTIME EMAIL_USE_TLS MANAGERS SERVER_EMAIL Error reporting DEFAULT_EXCEPTION_REPORTER DEFAULT_EXCEPTION_REPORTER_FILTER IGNORABLE_404_URLS MANAGERS SILENCED_SYSTEM_CHECKS File uploads DEFAULT_FILE_STORAGE FILE_UPLOAD_HANDLERS FILE_UPLOAD_MAX_MEMORY_SIZE FILE_UPLOAD_PERMISSIONS FILE_UPLOAD_TEMP_DIR MEDIA_ROOT MEDIA_URL Forms FORM_RENDERER Globalization (i18n/l10n) DATE_FORMAT DATE_INPUT_FORMATS DATETIME_FORMAT DATETIME_INPUT_FORMATS DECIMAL_SEPARATOR FIRST_DAY_OF_WEEK FORMAT_MODULE_PATH LANGUAGE_CODE LANGUAGE_COOKIE_AGE LANGUAGE_COOKIE_DOMAIN LANGUAGE_COOKIE_HTTPONLY LANGUAGE_COOKIE_NAME LANGUAGE_COOKIE_PATH LANGUAGE_COOKIE_SAMESITE LANGUAGE_COOKIE_SECURE LANGUAGES LANGUAGES_BIDI LOCALE_PATHS MONTH_DAY_FORMAT NUMBER_GROUPING SHORT_DATE_FORMAT SHORT_DATETIME_FORMAT THOUSAND_SEPARATOR TIME_FORMAT TIME_INPUT_FORMATS TIME_ZONE USE_I18N USE_L10N USE_THOUSAND_SEPARATOR USE_TZ YEAR_MONTH_FORMAT HTTP DATA_UPLOAD_MAX_MEMORY_SIZE DATA_UPLOAD_MAX_NUMBER_FIELDS DEFAULT_CHARSET DISALLOWED_USER_AGENTS FORCE_SCRIPT_NAME INTERNAL_IPS MIDDLEWARE Security SECURE_CONTENT_TYPE_NOSNIFF SECURE_CROSS_ORIGIN_OPENER_POLICY SECURE_HSTS_INCLUDE_SUBDOMAINS SECURE_HSTS_PRELOAD SECURE_HSTS_SECONDS SECURE_PROXY_SSL_HEADER SECURE_REDIRECT_EXEMPT SECURE_REFERRER_POLICY SECURE_SSL_HOST SECURE_SSL_REDIRECT SIGNING_BACKEND USE_X_FORWARDED_HOST USE_X_FORWARDED_PORT WSGI_APPLICATION Logging LOGGING LOGGING_CONFIG Models ABSOLUTE_URL_OVERRIDES FIXTURE_DIRS INSTALLED_APPS Security Cross Site Request Forgery Protection CSRF_COOKIE_DOMAIN CSRF_COOKIE_NAME CSRF_COOKIE_PATH CSRF_COOKIE_SAMESITE CSRF_COOKIE_SECURE CSRF_FAILURE_VIEW CSRF_HEADER_NAME CSRF_TRUSTED_ORIGINS CSRF_USE_SESSIONS SECRET_KEY X_FRAME_OPTIONS Serialization DEFAULT_CHARSET SERIALIZATION_MODULES Templates TEMPLATES Testing Database: TEST
TEST_NON_SERIALIZED_APPS TEST_RUNNER URLs APPEND_SLASH PREPEND_WWW ROOT_URLCONF | |
doc_28535 |
Return alias for index. Returns
Index
Index of the Series. | |
doc_28536 |
Return the number of days in the month. Examples
>>> ts = pd.Timestamp(2020, 3, 14)
>>> ts.days_in_month
31 | |
doc_28537 | Return True if path is an existing regular file. This follows symbolic links, so both islink() and isfile() can be true for the same path. Changed in version 3.6: Accepts a path-like object. | |
doc_28538 |
Write out format foo. This method is normally called via Figure.savefig and FigureCanvasBase.print_figure, which take care of setting the figure facecolor, edgecolor, and dpi to the desired output values, and will restore them to the original values. Therefore, print_foo does not need to handle these settings. | |
doc_28539 | Returns an iterator over all modules in the network. Yields
Module – a module in the network Note Duplicate modules are returned only once. In the following example, l will be returned only once. Example: >>> l = nn.Linear(2, 2)
>>> net = nn.Sequential(l, l)
>>> for idx, m in enumerate(net.modules()):
print(idx, '->', m)
0 -> Sequential(
(0): Linear(in_features=2, out_features=2, bias=True)
(1): Linear(in_features=2, out_features=2, bias=True)
)
1 -> Linear(in_features=2, out_features=2, bias=True) | |
doc_28540 | class dict(mapping, **kwarg)
class dict(iterable, **kwarg)
Return a new dictionary initialized from an optional positional argument and a possibly empty set of keyword arguments. Dictionaries can be created by several means: Use a comma-separated list of key: value pairs within braces: {'jack': 4098, 'sjoerd': 4127} or {4098: 'jack', 4127: 'sjoerd'}
Use a dict comprehension: {}, {x: x ** 2 for x in range(10)}
Use the type constructor: dict(), dict([('foo', 100), ('bar', 200)]), dict(foo=100, bar=200)
If no positional argument is given, an empty dictionary is created. If a positional argument is given and it is a mapping object, a dictionary is created with the same key-value pairs as the mapping object. Otherwise, the positional argument must be an iterable object. Each item in the iterable must itself be an iterable with exactly two objects. The first object of each item becomes a key in the new dictionary, and the second object the corresponding value. If a key occurs more than once, the last value for that key becomes the corresponding value in the new dictionary. If keyword arguments are given, the keyword arguments and their values are added to the dictionary created from the positional argument. If a key being added is already present, the value from the keyword argument replaces the value from the positional argument. To illustrate, the following examples all return a dictionary equal to {"one": 1, "two": 2, "three": 3}: >>> a = dict(one=1, two=2, three=3)
>>> b = {'one': 1, 'two': 2, 'three': 3}
>>> c = dict(zip(['one', 'two', 'three'], [1, 2, 3]))
>>> d = dict([('two', 2), ('one', 1), ('three', 3)])
>>> e = dict({'three': 3, 'one': 1, 'two': 2})
>>> f = dict({'one': 1, 'three': 3}, two=2)
>>> a == b == c == d == e == f
True
Providing keyword arguments as in the first example only works for keys that are valid Python identifiers. Otherwise, any valid keys can be used. These are the operations that dictionaries support (and therefore, custom mapping types should support too):
list(d)
Return a list of all the keys used in the dictionary d.
len(d)
Return the number of items in the dictionary d.
d[key]
Return the item of d with key key. Raises a KeyError if key is not in the map. If a subclass of dict defines a method __missing__() and key is not present, the d[key] operation calls that method with the key key as argument. The d[key] operation then returns or raises whatever is returned or raised by the __missing__(key) call. No other operations or methods invoke __missing__(). If __missing__() is not defined, KeyError is raised. __missing__() must be a method; it cannot be an instance variable: >>> class Counter(dict):
... def __missing__(self, key):
... return 0
>>> c = Counter()
>>> c['red']
0
>>> c['red'] += 1
>>> c['red']
1
The example above shows part of the implementation of collections.Counter. A different __missing__ method is used by collections.defaultdict.
d[key] = value
Set d[key] to value.
del d[key]
Remove d[key] from d. Raises a KeyError if key is not in the map.
key in d
Return True if d has a key key, else False.
key not in d
Equivalent to not key in d.
iter(d)
Return an iterator over the keys of the dictionary. This is a shortcut for iter(d.keys()).
clear()
Remove all items from the dictionary.
copy()
Return a shallow copy of the dictionary.
classmethod fromkeys(iterable[, value])
Create a new dictionary with keys from iterable and values set to value. fromkeys() is a class method that returns a new dictionary. value defaults to None. All of the values refer to just a single instance, so it generally doesn’t make sense for value to be a mutable object such as an empty list. To get distinct values, use a dict comprehension instead.
get(key[, default])
Return the value for key if key is in the dictionary, else default. If default is not given, it defaults to None, so that this method never raises a KeyError.
items()
Return a new view of the dictionary’s items ((key, value) pairs). See the documentation of view objects.
keys()
Return a new view of the dictionary’s keys. See the documentation of view objects.
pop(key[, default])
If key is in the dictionary, remove it and return its value, else return default. If default is not given and key is not in the dictionary, a KeyError is raised.
popitem()
Remove and return a (key, value) pair from the dictionary. Pairs are returned in LIFO order. popitem() is useful to destructively iterate over a dictionary, as often used in set algorithms. If the dictionary is empty, calling popitem() raises a KeyError. Changed in version 3.7: LIFO order is now guaranteed. In prior versions, popitem() would return an arbitrary key/value pair.
reversed(d)
Return a reverse iterator over the keys of the dictionary. This is a shortcut for reversed(d.keys()). New in version 3.8.
setdefault(key[, default])
If key is in the dictionary, return its value. If not, insert key with a value of default and return default. default defaults to None.
update([other])
Update the dictionary with the key/value pairs from other, overwriting existing keys. Return None. update() accepts either another dictionary object or an iterable of key/value pairs (as tuples or other iterables of length two). If keyword arguments are specified, the dictionary is then updated with those key/value pairs: d.update(red=1, blue=2).
values()
Return a new view of the dictionary’s values. See the documentation of view objects. An equality comparison between one dict.values() view and another will always return False. This also applies when comparing dict.values() to itself: >>> d = {'a': 1}
>>> d.values() == d.values()
False
d | other
Create a new dictionary with the merged keys and values of d and other, which must both be dictionaries. The values of other take priority when d and other share keys. New in version 3.9.
d |= other
Update the dictionary d with keys and values from other, which may be either a mapping or an iterable of key/value pairs. The values of other take priority when d and other share keys. New in version 3.9.
Dictionaries compare equal if and only if they have the same (key,
value) pairs (regardless of ordering). Order comparisons (‘<’, ‘<=’, ‘>=’, ‘>’) raise TypeError. Dictionaries preserve insertion order. Note that updating a key does not affect the order. Keys added after deletion are inserted at the end. >>> d = {"one": 1, "two": 2, "three": 3, "four": 4}
>>> d
{'one': 1, 'two': 2, 'three': 3, 'four': 4}
>>> list(d)
['one', 'two', 'three', 'four']
>>> list(d.values())
[1, 2, 3, 4]
>>> d["one"] = 42
>>> d
{'one': 42, 'two': 2, 'three': 3, 'four': 4}
>>> del d["two"]
>>> d["two"] = None
>>> d
{'one': 42, 'three': 3, 'four': 4, 'two': None}
Changed in version 3.7: Dictionary order is guaranteed to be insertion order. This behavior was an implementation detail of CPython from 3.6. Dictionaries and dictionary views are reversible. >>> d = {"one": 1, "two": 2, "three": 3, "four": 4}
>>> d
{'one': 1, 'two': 2, 'three': 3, 'four': 4}
>>> list(reversed(d))
['four', 'three', 'two', 'one']
>>> list(reversed(d.values()))
[4, 3, 2, 1]
>>> list(reversed(d.items()))
[('four', 4), ('three', 3), ('two', 2), ('one', 1)]
Changed in version 3.8: Dictionaries are now reversible. | |
doc_28541 |
Integrate. Return a series instance that is the definite integral of the current series. Parameters
mnon-negative int
The number of integrations to perform.
karray_like
Integration constants. The first constant is applied to the first integration, the second to the second, and so on. The list of values must less than or equal to m in length and any missing values are set to zero.
lbndScalar
The lower bound of the definite integral. Returns
new_seriesseries
A new series representing the integral. The domain is the same as the domain of the integrated series. | |
doc_28542 | See Migration guide for more details. tf.compat.v1.debugging.assert_equal
tf.compat.v1.assert_equal(
x, y, data=None, summarize=None, message=None, name=None
)
This condition holds if for every pair of (possibly broadcast) elements x[i], y[i], we have x[i] == y[i]. If both x and y are empty, this is trivially satisfied. When running in graph mode, you should add a dependency on this operation to ensure that it runs. Example of adding a dependency to an operation: with tf.control_dependencies([tf.compat.v1.assert_equal(x, y)]):
output = tf.reduce_sum(x)
Args
x Numeric Tensor.
y Numeric Tensor, same dtype as and broadcastable to x.
data The tensors to print out if the condition is False. Defaults to error message and first few entries of x, y.
summarize Print this many entries of each tensor.
message A string to prefix to the default message.
name A name for this operation (optional). Defaults to "assert_equal".
Returns Op that raises InvalidArgumentError if x == y is False.
Raises
InvalidArgumentError if the check can be performed immediately and x == y is False. The check can be performed immediately during eager execution or if x and y are statically known. Eager Compatibility returns None | |
doc_28543 | See Migration guide for more details. tf.compat.v1.raw_ops.RngSkip
tf.raw_ops.RngSkip(
resource, algorithm, delta, name=None
)
The state of the RNG after rng_skip(n) will be the same as that after stateful_uniform([n]) (or any other distribution). The actual increment added to the counter is an unspecified implementation detail.
Args
resource A Tensor of type resource. The handle of the resource variable that stores the state of the RNG.
algorithm A Tensor of type int64. The RNG algorithm.
delta A Tensor of type int64. The amount of advancement.
name A name for the operation (optional).
Returns The created Operation. | |
doc_28544 |
Return an array representing the indices of a grid. Compute an array where the subarrays contain index values 0, 1, … varying only along the corresponding axis. Parameters
dimensionssequence of ints
The shape of the grid.
dtypedtype, optional
Data type of the result.
sparseboolean, optional
Return a sparse representation of the grid instead of a dense representation. Default is False. New in version 1.17. Returns
gridone MaskedArray or tuple of MaskedArrays
If sparse is False:
Returns one array of grid indices, grid.shape = (len(dimensions),) + tuple(dimensions). If sparse is True:
Returns a tuple of arrays, with grid[i].shape = (1, ..., 1, dimensions[i], 1, ..., 1) with dimensions[i] in the ith place See also
mgrid, ogrid, meshgrid
Notes The output shape in the dense case is obtained by prepending the number of dimensions in front of the tuple of dimensions, i.e. if dimensions is a tuple (r0, ..., rN-1) of length N, the output shape is (N, r0, ..., rN-1). The subarrays grid[k] contains the N-D array of indices along the k-th axis. Explicitly: grid[k, i0, i1, ..., iN-1] = ik
Examples >>> grid = np.indices((2, 3))
>>> grid.shape
(2, 2, 3)
>>> grid[0] # row indices
array([[0, 0, 0],
[1, 1, 1]])
>>> grid[1] # column indices
array([[0, 1, 2],
[0, 1, 2]])
The indices can be used as an index into an array. >>> x = np.arange(20).reshape(5, 4)
>>> row, col = np.indices((2, 3))
>>> x[row, col]
array([[0, 1, 2],
[4, 5, 6]])
Note that it would be more straightforward in the above example to extract the required elements directly with x[:2, :3]. If sparse is set to true, the grid will be returned in a sparse representation. >>> i, j = np.indices((2, 3), sparse=True)
>>> i.shape
(2, 1)
>>> j.shape
(1, 3)
>>> i # row indices
array([[0],
[1]])
>>> j # column indices
array([[0, 1, 2]]) | |
doc_28545 | Returns the name of the tab element at position x, y, or the empty string if none. | |
doc_28546 |
Customize compiler using distutils command. Parameters
cmdclass instance
An instance inheriting from distutils.cmd.Command.
ignoresequence of str, optional
List of CCompiler commands (without 'set_') that should not be altered. Strings that are checked for are: ('include_dirs', 'define', 'undef', 'libraries', 'library_dirs',
'rpath', 'link_objects'). Returns
None | |
doc_28547 | This method is called unconditionally after tearDown(), or after setUp() if setUp() raises an exception. It is responsible for calling all the cleanup functions added by addCleanup(). If you need cleanup functions to be called prior to tearDown() then you can call doCleanups() yourself. doCleanups() pops methods off the stack of cleanup functions one at a time, so it can be called at any time. New in version 3.1. | |
doc_28548 | Packer is the class for packing data into XDR representation. The Packer class is instantiated with no arguments. | |
doc_28549 | See Migration guide for more details. tf.compat.v1.raw_ops.EncodeJpeg
tf.raw_ops.EncodeJpeg(
image, format='', quality=95, progressive=False, optimize_size=False,
chroma_downsampling=True, density_unit='in', x_density=300,
y_density=300, xmp_metadata='', name=None
)
image is a 3-D uint8 Tensor of shape [height, width, channels]. The attr format can be used to override the color format of the encoded output. Values can be:
'': Use a default format based on the number of channels in the image.
grayscale: Output a grayscale JPEG image. The channels dimension of image must be 1.
rgb: Output an RGB JPEG image. The channels dimension of image must be 3. If format is not specified or is the empty string, a default format is picked in function of the number of channels in image: 1: Output a grayscale image. 3: Output an RGB image.
Args
image A Tensor of type uint8. 3-D with shape [height, width, channels].
format An optional string from: "", "grayscale", "rgb". Defaults to "". Per pixel image format.
quality An optional int. Defaults to 95. Quality of the compression from 0 to 100 (higher is better and slower).
progressive An optional bool. Defaults to False. If True, create a JPEG that loads progressively (coarse to fine).
optimize_size An optional bool. Defaults to False. If True, spend CPU/RAM to reduce size with no quality change.
chroma_downsampling An optional bool. Defaults to True. See http://en.wikipedia.org/wiki/Chroma_subsampling
density_unit An optional string from: "in", "cm". Defaults to "in". Unit used to specify x_density and y_density: pixels per inch ('in') or centimeter ('cm').
x_density An optional int. Defaults to 300. Horizontal pixels per density unit.
y_density An optional int. Defaults to 300. Vertical pixels per density unit.
xmp_metadata An optional string. Defaults to "". If not empty, embed this XMP metadata in the image header.
name A name for the operation (optional).
Returns A Tensor of type string. | |
doc_28550 | See Migration guide for more details. tf.compat.v1.raw_ops.TensorScatterMax
tf.raw_ops.TensorScatterMax(
tensor, indices, updates, name=None
)
Args
tensor A Tensor. Tensor to update.
indices A Tensor. Must be one of the following types: int32, int64. Index tensor.
updates A Tensor. Must have the same type as tensor. Updates to scatter into output.
name A name for the operation (optional).
Returns A Tensor. Has the same type as tensor. | |
doc_28551 |
Return a matrix of random values with given shape. Create a matrix of the given shape and propagate it with random samples from a uniform distribution over [0, 1). Parameters
*argsArguments
Shape of the output. If given as N integers, each integer specifies the size of one dimension. If given as a tuple, this tuple gives the complete shape. Returns
outndarray
The matrix of random values with shape given by *args. See also
randn, numpy.random.RandomState.rand
Examples >>> np.random.seed(123)
>>> import numpy.matlib
>>> np.matlib.rand(2, 3)
matrix([[0.69646919, 0.28613933, 0.22685145],
[0.55131477, 0.71946897, 0.42310646]])
>>> np.matlib.rand((2, 3))
matrix([[0.9807642 , 0.68482974, 0.4809319 ],
[0.39211752, 0.34317802, 0.72904971]])
If the first argument is a tuple, other arguments are ignored: >>> np.matlib.rand((2, 3), 4)
matrix([[0.43857224, 0.0596779 , 0.39804426],
[0.73799541, 0.18249173, 0.17545176]]) | |
doc_28552 |
Return the Transform instance mapping patch coordinates to data coordinates. For example, one may define a patch of a circle which represents a radius of 5 by providing coordinates for a unit circle, and a transform which scales the coordinates (the patch coordinate) by 5. | |
doc_28553 | Return the reconstituted object hierarchy of the pickled representation data of an object. data must be a bytes-like object. The protocol version of the pickle is detected automatically, so no protocol argument is needed. Bytes past the pickled representation of the object are ignored. Arguments file, fix_imports, encoding, errors, strict and buffers have the same meaning as in the Unpickler constructor. Changed in version 3.8: The buffers argument was added. | |
doc_28554 |
Whether the categories have an ordered relationship. | |
doc_28555 | Blueprint local JSON encoder class to use. Set to None to use the app’s json_encoder. | |
doc_28556 | See Migration guide for more details. tf.compat.v1.raw_ops.RetrieveTPUEmbeddingProximalAdagradParameters
tf.raw_ops.RetrieveTPUEmbeddingProximalAdagradParameters(
num_shards, shard_id, table_id=-1, table_name='', config='',
name=None
)
An op that retrieves optimization parameters from embedding to host memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to retrieve updated parameters before saving a checkpoint.
Args
num_shards An int.
shard_id An int.
table_id An optional int. Defaults to -1.
table_name An optional string. Defaults to "".
config An optional string. Defaults to "".
name A name for the operation (optional).
Returns A tuple of Tensor objects (parameters, accumulators). parameters A Tensor of type float32.
accumulators A Tensor of type float32. | |
doc_28557 |
Randomly zero out entire channels (a channel is a 2D feature map, e.g., the jj -th channel of the ii -th sample in the batched input is a 2D tensor input[i,j]\text{input}[i, j] ) of the input tensor). Each channel will be zeroed out independently on every forward call with probability p using samples from a Bernoulli distribution. See Dropout2d for details. Parameters
p – probability of a channel to be zeroed. Default: 0.5
training – apply dropout if is True. Default: True
inplace – If set to True, will do this operation in-place. Default: False | |
doc_28558 |
TransformerDecoderLayer is made up of self-attn, multi-head-attn and feedforward network. This standard decoder layer is based on the paper “Attention Is All You Need”. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems, pages 6000-6010. Users may modify or implement in a different way during application. Parameters
d_model – the number of expected features in the input (required).
nhead – the number of heads in the multiheadattention models (required).
dim_feedforward – the dimension of the feedforward network model (default=2048).
dropout – the dropout value (default=0.1).
activation – the activation function of intermediate layer, relu or gelu (default=relu). Examples::
>>> decoder_layer = nn.TransformerDecoderLayer(d_model=512, nhead=8)
>>> memory = torch.rand(10, 32, 512)
>>> tgt = torch.rand(20, 32, 512)
>>> out = decoder_layer(tgt, memory)
forward(tgt, memory, tgt_mask=None, memory_mask=None, tgt_key_padding_mask=None, memory_key_padding_mask=None) [source]
Pass the inputs (and mask) through the decoder layer. Parameters
tgt – the sequence to the decoder layer (required).
memory – the sequence from the last layer of the encoder (required).
tgt_mask – the mask for the tgt sequence (optional).
memory_mask – the mask for the memory sequence (optional).
tgt_key_padding_mask – the mask for the tgt keys per batch (optional).
memory_key_padding_mask – the mask for the memory keys per batch (optional). Shape:
see the docs in Transformer class. | |
doc_28559 |
Return number of unique elements in the group. Returns
Series
Number of unique values within each group. | |
doc_28560 | Make this feature the current feature of msilib. New components are automatically added to the default feature, unless a feature is explicitly specified. | |
doc_28561 | A binary stream using an in-memory bytes buffer. It inherits BufferedIOBase. The buffer is discarded when the close() method is called. The optional argument initial_bytes is a bytes-like object that contains initial data. BytesIO provides or overrides these methods in addition to those from BufferedIOBase and IOBase:
getbuffer()
Return a readable and writable view over the contents of the buffer without copying them. Also, mutating the view will transparently update the contents of the buffer: >>> b = io.BytesIO(b"abcdef")
>>> view = b.getbuffer()
>>> view[2:4] = b"56"
>>> b.getvalue()
b'ab56ef'
Note As long as the view exists, the BytesIO object cannot be resized or closed. New in version 3.2.
getvalue()
Return bytes containing the entire contents of the buffer.
read1([size])
In BytesIO, this is the same as read(). Changed in version 3.7: The size argument is now optional.
readinto1(b)
In BytesIO, this is the same as readinto(). New in version 3.5. | |
doc_28562 | See Migration guide for more details. tf.compat.v1.signal.hamming_window
tf.signal.hamming_window(
window_length, periodic=True, dtype=tf.dtypes.float32, name=None
)
Args
window_length A scalar Tensor indicating the window length to generate.
periodic A bool Tensor indicating whether to generate a periodic or symmetric window. Periodic windows are typically used for spectral analysis while symmetric windows are typically used for digital filter design.
dtype The data type to produce. Must be a floating point type.
name An optional name for the operation.
Returns A Tensor of shape [window_length] of type dtype.
Raises
ValueError If dtype is not a floating point type. | |
doc_28563 | New in version 3.9. Status code returned by server. | |
doc_28564 |
Return Integer division and modulo of series and other, element-wise (binary operator divmod). Equivalent to divmod(series, other), but with support to substitute a fill_value for missing data in either one of the inputs. Parameters
other:Series or scalar value
fill_value:None or float value, default None (NaN)
Fill existing missing (NaN) values, and any new element needed for successful Series alignment, with this value before computation. If data in both corresponding Series locations is missing the result of filling (at that location) will be missing.
level:int or name
Broadcast across a level, matching Index values on the passed MultiIndex level. Returns
2-Tuple of Series
The result of the operation. See also Series.rdivmod
Reverse of the Integer division and modulo operator, see Python documentation for more details. Examples
>>> a = pd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a 1.0
b 1.0
c 1.0
d NaN
dtype: float64
>>> b = pd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a 1.0
b NaN
d 1.0
e NaN
dtype: float64
>>> a.divmod(b, fill_value=0)
(a 1.0
b NaN
c NaN
d 0.0
e NaN
dtype: float64,
a 0.0
b NaN
c NaN
d 0.0
e NaN
dtype: float64) | |
doc_28565 | Returns True if the user has each of the specified permissions, where each perm is in the format "<app label>.<permission codename>". If the user is inactive, this method will always return False. For an active superuser, this method will always return True. If obj is passed in, this method won’t check for permissions for the model, but for the specific object. | |
doc_28566 | Return date object with same year, month and day. | |
doc_28567 | Restrict the process with PID pid (or the current process if zero) to a set of CPUs. mask is an iterable of integers representing the set of CPUs to which the process should be restricted. | |
doc_28568 | Return the highest index in the string where substring sub is found, such that sub is contained within s[start:end]. Optional arguments start and end are interpreted as in slice notation. Return -1 on failure. | |
doc_28569 | In-place version of greater_equal(). | |
doc_28570 |
Bases: object An Axes positioning class. The divider is initialized with lists of horizontal and vertical sizes (mpl_toolkits.axes_grid1.axes_size) based on which a given rectangular area will be divided. The new_locator method then creates a callable object that can be used as the axes_locator of the axes. Parameters
figFigure
postuple of 4 floats
Position of the rectangle that will be divided.
horizontallist of axes_size
Sizes for horizontal division.
verticallist of axes_size
Sizes for vertical division.
aspectbool
Whether overall rectangular area is reduced so that the relative part of the horizontal and vertical scales have the same scale.
anchor{'C', 'SW', 'S', 'SE', 'E', 'NE', 'N', 'NW', 'W'}
Placement of the reduced rectangle, when aspect is True. add_auto_adjustable_area(use_axes, pad=0.1, adjust_dirs=None)[source]
append_size(position, size)[source]
get_anchor()[source]
Return the anchor.
get_aspect()[source]
Return aspect.
get_horizontal()[source]
Return horizontal sizes.
get_horizontal_sizes(renderer)[source]
get_locator()[source]
get_position()[source]
Return the position of the rectangle.
get_position_runtime(ax, renderer)[source]
get_vertical()[source]
Return vertical sizes.
get_vertical_sizes(renderer)[source]
get_vsize_hsize()[source]
[Deprecated] Notes Deprecated since version 3.5:
locate(nx, ny, nx1=None, ny1=None, axes=None, renderer=None)[source]
Parameters
nx, nx1int
Integers specifying the column-position of the cell. When nx1 is None, a single nx-th column is specified. Otherwise location of columns spanning between nx to nx1 (but excluding nx1-th column) is specified.
ny, ny1int
Same as nx and nx1, but for row positions. axes
renderer
new_locator(nx, ny, nx1=None, ny1=None)[source]
Return a new AxesLocator for the specified cell. Parameters
nx, nx1int
Integers specifying the column-position of the cell. When nx1 is None, a single nx-th column is specified. Otherwise location of columns spanning between nx to nx1 (but excluding nx1-th column) is specified.
ny, ny1int
Same as nx and nx1, but for row positions.
set_anchor(anchor)[source]
Parameters
anchor(float, float) or {'C', 'SW', 'S', 'SE', 'E', 'NE', ...}
Either an (x, y) pair of relative coordinates (0 is left or bottom, 1 is right or top), 'C' (center), or a cardinal direction ('SW', southwest, is bottom left, etc.). See also Axes.set_anchor
set_aspect(aspect=False)[source]
Parameters
aspectbool
set_horizontal(h)[source]
Parameters
hlist of axes_size
sizes for horizontal division
set_locator(_locator)[source]
set_position(pos)[source]
Set the position of the rectangle. Parameters
postuple of 4 floats
position of the rectangle that will be divided
set_vertical(v)[source]
Parameters
vlist of axes_size
sizes for vertical division | |
doc_28571 |
Bases: matplotlib.container.Container Container for the artists of bar plots (e.g. created by Axes.bar). The container can be treated as a tuple of the patches themselves. Additionally, you can access these and further parameters by the attributes. Attributes
patcheslist of Rectangle
The artists of the bars.
errorbarNone or ErrorbarContainer
A container for the error bar artists if error bars are present. None otherwise.
datavaluesNone or array-like
The underlying data values corresponding to the bars.
orientation{'vertical', 'horizontal'}, default: None
If 'vertical', the bars are assumed to be vertical. If 'horizontal', the bars are assumed to be horizontal. | |
doc_28572 | Register an archiver for the format name. function is the callable that will be used to unpack archives. The callable will receive the base_name of the file to create, followed by the base_dir (which defaults to os.curdir) to start archiving from. Further arguments are passed as keyword arguments: owner, group, dry_run and logger (as passed in make_archive()). If given, extra_args is a sequence of (name, value) pairs that will be used as extra keywords arguments when the archiver callable is used. description is used by get_archive_formats() which returns the list of archivers. Defaults to an empty string. | |
doc_28573 | The number of threads required to pass the barrier. | |
doc_28574 | Computes the complementary error function of each element of input. The complementary error function is defined as follows: erfc(x)=1−2π∫0xe−t2dt\mathrm{erfc}(x) = 1 - \frac{2}{\sqrt{\pi}} \int_{0}^{x} e^{-t^2} dt
Parameters
input (Tensor) – the input tensor. Keyword Arguments
out (Tensor, optional) – the output tensor. Example: >>> torch.erfc(torch.tensor([0, -1., 10.]))
tensor([ 1.0000, 1.8427, 0.0000]) | |
doc_28575 |
Add a centered suptitle to the figure. Parameters
tstr
The suptitle text.
xfloat, default: 0.5
The x location of the text in figure coordinates.
yfloat, default: 0.98
The y location of the text in figure coordinates.
horizontalalignment, ha{'center', 'left', 'right'}, default: center
The horizontal alignment of the text relative to (x, y).
verticalalignment, va{'top', 'center', 'bottom', 'baseline'}, default: top
The vertical alignment of the text relative to (x, y).
fontsize, sizedefault: rcParams["figure.titlesize"] (default: 'large')
The font size of the text. See Text.set_size for possible values.
fontweight, weightdefault: rcParams["figure.titleweight"] (default: 'normal')
The font weight of the text. See Text.set_weight for possible values. Returns
text
The Text instance of the suptitle. Other Parameters
fontpropertiesNone or dict, optional
A dict of font properties. If fontproperties is given the default values for font size and weight are taken from the FontProperties defaults. rcParams["figure.titlesize"] (default: 'large') and rcParams["figure.titleweight"] (default: 'normal') are ignored in this case. **kwargs
Additional kwargs are matplotlib.text.Text properties. | |
doc_28576 |
Return the Transform instance used by this artist offset. | |
doc_28577 | Schedule callback to be called after the given delay number of seconds (can be either an int or a float). An instance of asyncio.TimerHandle is returned which can be used to cancel the callback. callback will be called exactly once. If two callbacks are scheduled for exactly the same time, the order in which they are called is undefined. The optional positional args will be passed to the callback when it is called. If you want the callback to be called with keyword arguments use functools.partial(). An optional keyword-only context argument allows specifying a custom contextvars.Context for the callback to run in. The current context is used when no context is provided. Changed in version 3.7: The context keyword-only parameter was added. See PEP 567 for more details. Changed in version 3.8: In Python 3.7 and earlier with the default event loop implementation, the delay could not exceed one day. This has been fixed in Python 3.8. | |
doc_28578 | See Migration guide for more details. tf.compat.v1.estimator.MultiHead
tf.estimator.MultiHead(
heads, head_weights=None
)
This class merges the output of multiple Head objects. Specifically: For training, sums losses of each head, calls train_op_fn with this final loss. For eval, merges metrics by adding head.name suffix to the keys in eval metrics, such as precision/head1.name, precision/head2.name. For prediction, merges predictions and updates keys in prediction dict to a 2-tuple, (head.name, prediction_key). Merges export_outputs such that by default the first head is served. Usage:
head1 = tf.estimator.MultiLabelHead(n_classes=2, name='head1')
head2 = tf.estimator.MultiLabelHead(n_classes=3, name='head2')
multi_head = tf.estimator.MultiHead([head1, head2])
logits = {
'head1': np.array([[-10., 10.], [-15., 10.]], dtype=np.float32),
'head2': np.array([[20., -20., 20.], [-30., 20., -20.]],
dtype=np.float32),}
labels = {
'head1': np.array([[1, 0], [1, 1]], dtype=np.int64),
'head2': np.array([[0, 1, 0], [1, 1, 0]], dtype=np.int64),}
features = {'x': np.array(((42,),), dtype=np.float32)}
# For large logits, sigmoid cross entropy loss is approximated as:
# loss = labels * (logits < 0) * (-logits) +
# (1 - labels) * (logits > 0) * logits =>
# head1: expected_unweighted_loss = [[10., 10.], [15., 0.]]
# loss1 = ((10 + 10) / 2 + (15 + 0) / 2) / 2 = 8.75
# head2: expected_unweighted_loss = [[20., 20., 20.], [30., 0., 0]]
# loss2 = ((20 + 20 + 20) / 3 + (30 + 0 + 0) / 3) / 2 = 15.00
# loss = loss1 + loss2 = 8.75 + 15.00 = 23.75
loss = multi_head.loss(labels, logits, features=features)
print('{:.2f}'.format(loss.numpy()))
23.75
eval_metrics = multi_head.metrics()
updated_metrics = multi_head.update_metrics(
eval_metrics, features, logits, labels)
for k in sorted(updated_metrics):
print('{} : {:.2f}'.format(k, updated_metrics[k].result().numpy()))
auc/head1 : 0.17
auc/head2 : 0.33
auc_precision_recall/head1 : 0.60
auc_precision_recall/head2 : 0.40
average_loss/head1 : 8.75
average_loss/head2 : 15.00
loss/head1 : 8.75
loss/head2 : 15.00
preds = multi_head.predictions(logits)
print(preds[('head1', 'logits')])
tf.Tensor(
[[-10. 10.]
[-15. 10.]], shape=(2, 2), dtype=float32)
Usage with a canned estimator: # In `input_fn`, specify labels as a dict keyed by head name:
def input_fn():
features = ...
labels1 = ...
labels2 = ...
return features, {'head1.name': labels1, 'head2.name': labels2}
# In `model_fn`, specify logits as a dict keyed by head name:
def model_fn(features, labels, mode):
# Create simple heads and specify head name.
head1 = tf.estimator.MultiClassHead(n_classes=3, name='head1')
head2 = tf.estimator.BinaryClassHead(name='head2')
# Create MultiHead from two simple heads.
head = tf.estimator.MultiHead([head1, head2])
# Create logits for each head, and combine them into a dict.
logits1, logits2 = logit_fn()
logits = {'head1.name': logits1, 'head2.name': logits2}
# Return the merged EstimatorSpec
return head.create_estimator_spec(..., logits=logits, ...)
# Create an estimator with this model_fn.
estimator = tf.estimator.Estimator(model_fn=model_fn)
estimator.train(input_fn=input_fn)
Also supports logits as a Tensor of shape [D0, D1, ... DN, logits_dimension]. It will split the Tensor along the last dimension and distribute it appropriately among the heads. E.g.: # Input logits.
logits = np.array([[-1., 1., 2., -2., 2.], [-1.5, 1., -3., 2., -2.]],
dtype=np.float32)
# Suppose head1 and head2 have the following logits dimension.
head1.logits_dimension = 2
head2.logits_dimension = 3
# After splitting, the result will be:
logits_dict = {'head1_name': [[-1., 1.], [-1.5, 1.]],
'head2_name': [[2., -2., 2.], [-3., 2., -2.]]}
Usage: def model_fn(features, labels, mode):
# Create simple heads and specify head name.
head1 = tf.estimator.MultiClassHead(n_classes=3, name='head1')
head2 = tf.estimator.BinaryClassHead(name='head2')
# Create multi-head from two simple heads.
head = tf.estimator.MultiHead([head1, head2])
# Create logits for the multihead. The result of logits is a `Tensor`.
logits = logit_fn(logits_dimension=head.logits_dimension)
# Return the merged EstimatorSpec
return head.create_estimator_spec(..., logits=logits, ...)
Args
heads List or tuple of Head instances. All heads must have name specified. The first head in the list is the default used at serving time.
head_weights Optional list of weights, same length as heads. Used when merging losses to calculate the weighted sum of losses from each head. If None, all losses are weighted equally.
Attributes
logits_dimension See base_head.Head for details.
loss_reduction See base_head.Head for details.
name See base_head.Head for details. Methods create_estimator_spec View source
create_estimator_spec(
features, mode, logits, labels=None, optimizer=None, trainable_variables=None,
train_op_fn=None, update_ops=None, regularization_losses=None
)
Returns a model_fn.EstimatorSpec.
Args
features Input dict of Tensor or SparseTensor objects.
mode Estimator's ModeKeys.
logits Input dict keyed by head name, or logits Tensor with shape [D0, D1, ... DN, logits_dimension]. For many applications, the Tensor shape is [batch_size, logits_dimension]. If logits is a Tensor, it will split the Tensor along the last dimension and distribute it appropriately among the heads. Check MultiHead for examples.
labels Input dict keyed by head name. For each head, the label value can be integer or string Tensor with shape matching its corresponding logits.labels is a required argument when mode equals TRAIN or EVAL.
optimizer An tf.keras.optimizers.Optimizer instance to optimize the loss in TRAIN mode. Namely, sets train_op = optimizer.get_updates(loss, trainable_variables), which updates variables to minimize loss.
trainable_variables A list or tuple of Variable objects to update to minimize loss. In Tensorflow 1.x, by default these are the list of variables collected in the graph under the key GraphKeys.TRAINABLE_VARIABLES. As Tensorflow 2.x doesn't have collections and GraphKeys, trainable_variables need to be passed explicitly here.
train_op_fn Function that takes a scalar loss Tensor and returns train_op. Used if optimizer is None.
update_ops A list or tuple of update ops to be run at training time. For example, layers such as BatchNormalization create mean and variance update ops that need to be run at training time. In Tensorflow 1.x, these are thrown into an UPDATE_OPS collection. As Tensorflow 2.x doesn't have collections, update_ops need to be passed explicitly here.
regularization_losses A list of additional scalar losses to be added to the training loss, such as regularization losses. These losses are usually expressed as a batch average, so for best results, in each head, users need to use the default loss_reduction=SUM_OVER_BATCH_SIZE to avoid scaling errors. Compared to the regularization losses for each head, this loss is to regularize the merged loss of all heads in multi head, and will be added to the overall training loss of multi head.
Returns A model_fn.EstimatorSpec instance.
Raises
ValueError If both train_op_fn and optimizer are None in TRAIN mode, or if both are set. If mode is not in Estimator's ModeKeys. loss View source
loss(
labels, logits, features=None, mode=None, regularization_losses=None
)
Returns regularized training loss. See base_head.Head for details. metrics View source
metrics(
regularization_losses=None
)
Creates metrics. See base_head.Head for details. predictions View source
predictions(
logits, keys=None
)
Create predictions. See base_head.Head for details. update_metrics View source
update_metrics(
eval_metrics, features, logits, labels, regularization_losses=None
)
Updates eval metrics. See base_head.Head for details. | |
doc_28579 |
Applies element-wise the function GELU(x)=x∗Φ(x)\text{GELU}(x) = x * \Phi(x) where Φ(x)\Phi(x) is the Cumulative Distribution Function for Gaussian Distribution. See Gaussian Error Linear Units (GELUs). | |
doc_28580 | Return a value equal to the first operand after rounding and having the exponent of the second operand. >>> Decimal('1.41421356').quantize(Decimal('1.000'))
Decimal('1.414')
Unlike other operations, if the length of the coefficient after the quantize operation would be greater than precision, then an InvalidOperation is signaled. This guarantees that, unless there is an error condition, the quantized exponent is always equal to that of the right-hand operand. Also unlike other operations, quantize never signals Underflow, even if the result is subnormal and inexact. If the exponent of the second operand is larger than that of the first then rounding may be necessary. In this case, the rounding mode is determined by the rounding argument if given, else by the given context argument; if neither argument is given the rounding mode of the current thread’s context is used. An error is returned whenever the resulting exponent is greater than Emax or less than Etiny. | |
doc_28581 | class sklearn.tree.ExtraTreeClassifier(*, criterion='gini', splitter='random', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, class_weight=None, ccp_alpha=0.0) [source]
An extremely randomized tree classifier. Extra-trees differ from classic decision trees in the way they are built. When looking for the best split to separate the samples of a node into two groups, random splits are drawn for each of the max_features randomly selected features and the best split among those is chosen. When max_features is set 1, this amounts to building a totally random decision tree. Warning: Extra-trees should only be used within ensemble methods. Read more in the User Guide. Parameters
criterion{“gini”, “entropy”}, default=”gini”
The function to measure the quality of a split. Supported criteria are “gini” for the Gini impurity and “entropy” for the information gain.
splitter{“random”, “best”}, default=”random”
The strategy used to choose the split at each node. Supported strategies are “best” to choose the best split and “random” to choose the best random split.
max_depthint, default=None
The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples.
min_samples_splitint or float, default=2
The minimum number of samples required to split an internal node: If int, then consider min_samples_split as the minimum number. If float, then min_samples_split is a fraction and ceil(min_samples_split * n_samples) are the minimum number of samples for each split. Changed in version 0.18: Added float values for fractions.
min_samples_leafint or float, default=1
The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least min_samples_leaf training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression. If int, then consider min_samples_leaf as the minimum number. If float, then min_samples_leaf is a fraction and ceil(min_samples_leaf * n_samples) are the minimum number of samples for each node. Changed in version 0.18: Added float values for fractions.
min_weight_fraction_leaffloat, default=0.0
The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided.
max_featuresint, float, {“auto”, “sqrt”, “log2”} or None, default=”auto”
The number of features to consider when looking for the best split: If int, then consider max_features features at each split. If float, then max_features is a fraction and int(max_features * n_features) features are considered at each split. If “auto”, then max_features=sqrt(n_features). If “sqrt”, then max_features=sqrt(n_features). If “log2”, then max_features=log2(n_features). If None, then max_features=n_features. Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than max_features features.
random_stateint, RandomState instance or None, default=None
Used to pick randomly the max_features used at each split. See Glossary for details.
max_leaf_nodesint, default=None
Grow a tree with max_leaf_nodes in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes.
min_impurity_decreasefloat, default=0.0
A node will be split if this split induces a decrease of the impurity greater than or equal to this value. The weighted impurity decrease equation is the following: N_t / N * (impurity - N_t_R / N_t * right_impurity
- N_t_L / N_t * left_impurity)
where N is the total number of samples, N_t is the number of samples at the current node, N_t_L is the number of samples in the left child, and N_t_R is the number of samples in the right child. N, N_t, N_t_R and N_t_L all refer to the weighted sum, if sample_weight is passed. New in version 0.19.
min_impurity_splitfloat, default=None
Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf. Deprecated since version 0.19: min_impurity_split has been deprecated in favor of min_impurity_decrease in 0.19. The default value of min_impurity_split has changed from 1e-7 to 0 in 0.23 and it will be removed in 1.0 (renaming of 0.25). Use min_impurity_decrease instead.
class_weightdict, list of dict or “balanced”, default=None
Weights associated with classes in the form {class_label: weight}. If None, all classes are supposed to have weight one. For multi-output problems, a list of dicts can be provided in the same order as the columns of y. Note that for multioutput (including multilabel) weights should be defined for each class of every column in its own dict. For example, for four-class multilabel classification weights should be [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of [{1:1}, {2:5}, {3:1}, {4:1}]. The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y)) For multi-output, the weights of each column of y will be multiplied. Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified.
ccp_alphanon-negative float, default=0.0
Complexity parameter used for Minimal Cost-Complexity Pruning. The subtree with the largest cost complexity that is smaller than ccp_alpha will be chosen. By default, no pruning is performed. See Minimal Cost-Complexity Pruning for details. New in version 0.22. Attributes
classes_ndarray of shape (n_classes,) or list of ndarray
The classes labels (single output problem), or a list of arrays of class labels (multi-output problem).
max_features_int
The inferred value of max_features.
n_classes_int or list of int
The number of classes (for single output problems), or a list containing the number of classes for each output (for multi-output problems).
feature_importances_ndarray of shape (n_features,)
Return the feature importances.
n_features_int
The number of features when fit is performed.
n_outputs_int
The number of outputs when fit is performed.
tree_Tree instance
The underlying Tree object. Please refer to help(sklearn.tree._tree.Tree) for attributes of Tree object and Understanding the decision tree structure for basic usage of these attributes. See also
ExtraTreeRegressor
An extremely randomized tree regressor.
sklearn.ensemble.ExtraTreesClassifier
An extra-trees classifier.
sklearn.ensemble.ExtraTreesRegressor
An extra-trees regressor. Notes The default values for the parameters controlling the size of the trees (e.g. max_depth, min_samples_leaf, etc.) lead to fully grown and unpruned trees which can potentially be very large on some data sets. To reduce memory consumption, the complexity and size of the trees should be controlled by setting those parameter values. References
1
P. Geurts, D. Ernst., and L. Wehenkel, “Extremely randomized trees”, Machine Learning, 63(1), 3-42, 2006. Examples >>> from sklearn.datasets import load_iris
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.ensemble import BaggingClassifier
>>> from sklearn.tree import ExtraTreeClassifier
>>> X, y = load_iris(return_X_y=True)
>>> X_train, X_test, y_train, y_test = train_test_split(
... X, y, random_state=0)
>>> extra_tree = ExtraTreeClassifier(random_state=0)
>>> cls = BaggingClassifier(extra_tree, random_state=0).fit(
... X_train, y_train)
>>> cls.score(X_test, y_test)
0.8947...
Methods
apply(X[, check_input]) Return the index of the leaf that each sample is predicted as.
cost_complexity_pruning_path(X, y[, …]) Compute the pruning path during Minimal Cost-Complexity Pruning.
decision_path(X[, check_input]) Return the decision path in the tree.
fit(X, y[, sample_weight, check_input, …]) Build a decision tree classifier from the training set (X, y).
get_depth() Return the depth of the decision tree.
get_n_leaves() Return the number of leaves of the decision tree.
get_params([deep]) Get parameters for this estimator.
predict(X[, check_input]) Predict class or regression value for X.
predict_log_proba(X) Predict class log-probabilities of the input samples X.
predict_proba(X[, check_input]) Predict class probabilities of the input samples X.
score(X, y[, sample_weight]) Return the mean accuracy on the given test data and labels.
set_params(**params) Set the parameters of this estimator.
apply(X, check_input=True) [source]
Return the index of the leaf that each sample is predicted as. New in version 0.17. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.
check_inputbool, default=True
Allow to bypass several input checking. Don’t use this parameter unless you know what you do. Returns
X_leavesarray-like of shape (n_samples,)
For each datapoint x in X, return the index of the leaf x ends up in. Leaves are numbered within [0; self.tree_.node_count), possibly with gaps in the numbering.
cost_complexity_pruning_path(X, y, sample_weight=None) [source]
Compute the pruning path during Minimal Cost-Complexity Pruning. See Minimal Cost-Complexity Pruning for details on the pruning process. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csc_matrix.
yarray-like of shape (n_samples,) or (n_samples, n_outputs)
The target values (class labels) as integers or strings.
sample_weightarray-like of shape (n_samples,), default=None
Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. Splits are also ignored if they would result in any single class carrying a negative weight in either child node. Returns
ccp_pathBunch
Dictionary-like object, with the following attributes.
ccp_alphasndarray
Effective alphas of subtree during pruning.
impuritiesndarray
Sum of the impurities of the subtree leaves for the corresponding alpha value in ccp_alphas.
decision_path(X, check_input=True) [source]
Return the decision path in the tree. New in version 0.18. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.
check_inputbool, default=True
Allow to bypass several input checking. Don’t use this parameter unless you know what you do. Returns
indicatorsparse matrix of shape (n_samples, n_nodes)
Return a node indicator CSR matrix where non zero elements indicates that the samples goes through the nodes.
property feature_importances_
Return the feature importances. The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance. Warning: impurity-based feature importances can be misleading for high cardinality features (many unique values). See sklearn.inspection.permutation_importance as an alternative. Returns
feature_importances_ndarray of shape (n_features,)
Normalized total reduction of criteria by feature (Gini importance).
fit(X, y, sample_weight=None, check_input=True, X_idx_sorted='deprecated') [source]
Build a decision tree classifier from the training set (X, y). Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csc_matrix.
yarray-like of shape (n_samples,) or (n_samples, n_outputs)
The target values (class labels) as integers or strings.
sample_weightarray-like of shape (n_samples,), default=None
Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. Splits are also ignored if they would result in any single class carrying a negative weight in either child node.
check_inputbool, default=True
Allow to bypass several input checking. Don’t use this parameter unless you know what you do.
X_idx_sorteddeprecated, default=”deprecated”
This parameter is deprecated and has no effect. It will be removed in 1.1 (renaming of 0.26). Deprecated since version 0.24. Returns
selfDecisionTreeClassifier
Fitted estimator.
get_depth() [source]
Return the depth of the decision tree. The depth of a tree is the maximum distance between the root and any leaf. Returns
self.tree_.max_depthint
The maximum depth of the tree.
get_n_leaves() [source]
Return the number of leaves of the decision tree. Returns
self.tree_.n_leavesint
Number of leaves.
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values.
predict(X, check_input=True) [source]
Predict class or regression value for X. For a classification model, the predicted class for each sample in X is returned. For a regression model, the predicted value based on X is returned. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.
check_inputbool, default=True
Allow to bypass several input checking. Don’t use this parameter unless you know what you do. Returns
yarray-like of shape (n_samples,) or (n_samples, n_outputs)
The predicted classes, or the predict values.
predict_log_proba(X) [source]
Predict class log-probabilities of the input samples X. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix. Returns
probandarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1
The class log-probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.
predict_proba(X, check_input=True) [source]
Predict class probabilities of the input samples X. The predicted class probability is the fraction of samples of the same class in a leaf. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.
check_inputbool, default=True
Allow to bypass several input checking. Don’t use this parameter unless you know what you do. Returns
probandarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1
The class probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.
score(X, y, sample_weight=None) [source]
Return the mean accuracy on the given test data and labels. In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted. Parameters
Xarray-like of shape (n_samples, n_features)
Test samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs)
True labels for X.
sample_weightarray-like of shape (n_samples,), default=None
Sample weights. Returns
scorefloat
Mean accuracy of self.predict(X) wrt. y.
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Estimator parameters. Returns
selfestimator instance
Estimator instance. | |
doc_28582 |
Remove minor ticks from the Axes. | |
doc_28583 | Get the value for an OpenGL flag for the current display gl_get_attribute(flag) -> value After calling pygame.display.set_mode() with the pygame.OPENGL flag, it is a good idea to check the value of any requested OpenGL attributes. See pygame.display.gl_set_attribute() for a list of valid flags. | |
doc_28584 |
Return the singular value decomposition (U, S, V) of a matrix, batches of matrices, or a sparse matrix AA such that A≈Udiag(S)VTA \approx U diag(S) V^T . In case MM is given, then SVD is computed for the matrix A−MA - M . Note The implementation is based on the Algorithm 5.1 from Halko et al, 2009. Note To obtain repeatable results, reset the seed for the pseudorandom number generator Note The input is assumed to be a low-rank matrix. Note In general, use the full-rank SVD implementation torch.svd for dense matrices due to its 10-fold higher performance characteristics. The low-rank SVD will be useful for huge sparse matrices that torch.svd cannot handle. Args::
A (Tensor): the input tensor of size (∗,m,n)(*, m, n) q (int, optional): a slightly overestimated rank of A. niter (int, optional): the number of subspace iterations to
conduct; niter must be a nonnegative integer, and defaults to 2 M (Tensor, optional): the input tensor’s mean of size
(∗,1,n)(*, 1, n) . References::
Nathan Halko, Per-Gunnar Martinsson, and Joel Tropp, Finding structure with randomness: probabilistic algorithms for constructing approximate matrix decompositions, arXiv:0909.4061 [math.NA; math.PR], 2009 (available at arXiv). | |
doc_28585 | Return True if the stream supports random access. If False, seek(), tell() and truncate() will raise OSError. | |
doc_28586 |
Initialize self. See help(type(self)) for accurate signature. | |
doc_28587 |
Return a Path for the current hatch. | |
doc_28588 | Unquotes a header value. (Reversal of quote_header_value()). This does not use the real unquoting but what browsers are actually using for quoting. Changelog New in version 0.5. Parameters
value (str) – the header value to unquote.
is_filename (bool) – The value represents a filename or path. Return type
str | |
doc_28589 | See Migration guide for more details. tf.compat.v1.random.experimental.stateless_split
tf.random.experimental.stateless_split(
seed, num=2
)
Example:
seed = [1, 2]
new_seeds = tf.random.experimental.stateless_split(seed, num=3)
print(new_seeds)
tf.Tensor(
[[1105988140 1738052849]
[-335576002 370444179]
[ 10670227 -246211131]], shape=(3, 2), dtype=int32)
tf.random.stateless_normal(shape=[3], seed=new_seeds[0, :])
<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.59835213, -0.9578608 ,
0.9002807 ], dtype=float32)>
Args
seed an RNG seed (a tensor with shape [2] and dtype int32 or int64). (When using XLA, only int32 is allowed.)
num optional, a positive integer or scalar tensor indicating the number of seeds to produce (default 2).
Returns A tensor with shape [num, 2] representing num new seeds. It will have the same dtype as seed (if seed doesn't have an explict dtype, the dtype will be determined by tf.convert_to_tensor). | |
doc_28590 | Sequence of bytecodes that access a constant. | |
doc_28591 | sklearn.linear_model.lasso_path(X, y, *, eps=0.001, n_alphas=100, alphas=None, precompute='auto', Xy=None, copy_X=True, coef_init=None, verbose=False, return_n_iter=False, positive=False, **params) [source]
Compute Lasso path with coordinate descent The Lasso optimization function varies for mono and multi-outputs. For mono-output tasks it is: (1 / (2 * n_samples)) * ||y - Xw||^2_2 + alpha * ||w||_1
For multi-output tasks it is: (1 / (2 * n_samples)) * ||Y - XW||^2_Fro + alpha * ||W||_21
Where: ||W||_21 = \sum_i \sqrt{\sum_j w_{ij}^2}
i.e. the sum of norm of each row. Read more in the User Guide. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Training data. Pass directly as Fortran-contiguous data to avoid unnecessary memory duplication. If y is mono-output then X can be sparse.
y{array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_outputs)
Target values
epsfloat, default=1e-3
Length of the path. eps=1e-3 means that alpha_min / alpha_max = 1e-3
n_alphasint, default=100
Number of alphas along the regularization path
alphasndarray, default=None
List of alphas where to compute the models. If None alphas are set automatically
precompute‘auto’, bool or array-like of shape (n_features, n_features), default=’auto’
Whether to use a precomputed Gram matrix to speed up calculations. If set to 'auto' let us decide. The Gram matrix can also be passed as argument.
Xyarray-like of shape (n_features,) or (n_features, n_outputs), default=None
Xy = np.dot(X.T, y) that can be precomputed. It is useful only when the Gram matrix is precomputed.
copy_Xbool, default=True
If True, X will be copied; else, it may be overwritten.
coef_initndarray of shape (n_features, ), default=None
The initial values of the coefficients.
verbosebool or int, default=False
Amount of verbosity.
return_n_iterbool, default=False
whether to return the number of iterations or not.
positivebool, default=False
If set to True, forces coefficients to be positive. (Only allowed when y.ndim == 1).
**paramskwargs
keyword arguments passed to the coordinate descent solver. Returns
alphasndarray of shape (n_alphas,)
The alphas along the path where models are computed.
coefsndarray of shape (n_features, n_alphas) or (n_outputs, n_features, n_alphas)
Coefficients along the path.
dual_gapsndarray of shape (n_alphas,)
The dual gaps at the end of the optimization for each alpha.
n_iterslist of int
The number of iterations taken by the coordinate descent optimizer to reach the specified tolerance for each alpha. See also
lars_path
Lasso
LassoLars
LassoCV
LassoLarsCV
sklearn.decomposition.sparse_encode
Notes For an example, see examples/linear_model/plot_lasso_coordinate_descent_path.py. To avoid unnecessary memory duplication the X argument of the fit method should be directly passed as a Fortran-contiguous numpy array. Note that in certain cases, the Lars solver may be significantly faster to implement this functionality. In particular, linear interpolation can be used to retrieve model coefficients between the values output by lars_path Examples Comparing lasso_path and lars_path with interpolation: >>> X = np.array([[1, 2, 3.1], [2.3, 5.4, 4.3]]).T
>>> y = np.array([1, 2, 3.1])
>>> # Use lasso_path to compute a coefficient path
>>> _, coef_path, _ = lasso_path(X, y, alphas=[5., 1., .5])
>>> print(coef_path)
[[0. 0. 0.46874778]
[0.2159048 0.4425765 0.23689075]]
>>> # Now use lars_path and 1D linear interpolation to compute the
>>> # same path
>>> from sklearn.linear_model import lars_path
>>> alphas, active, coef_path_lars = lars_path(X, y, method='lasso')
>>> from scipy import interpolate
>>> coef_path_continuous = interpolate.interp1d(alphas[::-1],
... coef_path_lars[:, ::-1])
>>> print(coef_path_continuous([5., 1., .5]))
[[0. 0. 0.46915237]
[0.2159048 0.4425765 0.23668876]]
Examples using sklearn.linear_model.lasso_path
Lasso and Elastic Net | |
doc_28592 | Validates that the upper bound of the range is not greater than limit_value. | |
doc_28593 | Call open() with method set to PATCH. Parameters
args (Any) –
kw (Any) – Return type
werkzeug.test.TestResponse | |
doc_28594 | See Migration guide for more details. tf.compat.v1.raw_ops.TensorListReserve
tf.raw_ops.TensorListReserve(
element_shape, num_elements, element_dtype, name=None
)
element_shape: the shape of the future elements of the list num_elements: the number of elements to reserve handle: the output list element_dtype: the desired type of elements in the list.
Args
element_shape A Tensor. Must be one of the following types: int32, int64.
num_elements A Tensor of type int32.
element_dtype A tf.DType.
name A name for the operation (optional).
Returns A Tensor of type variant. | |
doc_28595 | See Migration guide for more details. tf.compat.v1.app.flags.UnparsedFlagAccessError | |
doc_28596 | Disable the fault handler: uninstall the signal handlers installed by enable(). | |
doc_28597 |
Update the text properties. Valid keyword arguments are:
Property Description
agg_filter a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array
alpha scalar or None
animated bool
backgroundcolor color
bbox dict with properties for patches.FancyBboxPatch
clip_box unknown
clip_on unknown
clip_path unknown
color or c color
figure Figure
fontfamily or family {FONTNAME, 'serif', 'sans-serif', 'cursive', 'fantasy', 'monospace'}
fontproperties or font or font_properties font_manager.FontProperties or str or pathlib.Path
fontsize or size float or {'xx-small', 'x-small', 'small', 'medium', 'large', 'x-large', 'xx-large'}
fontstretch or stretch {a numeric value in range 0-1000, 'ultra-condensed', 'extra-condensed', 'condensed', 'semi-condensed', 'normal', 'semi-expanded', 'expanded', 'extra-expanded', 'ultra-expanded'}
fontstyle or style {'normal', 'italic', 'oblique'}
fontvariant or variant {'normal', 'small-caps'}
fontweight or weight {a numeric value in range 0-1000, 'ultralight', 'light', 'normal', 'regular', 'book', 'medium', 'roman', 'semibold', 'demibold', 'demi', 'bold', 'heavy', 'extra bold', 'black'}
gid str
horizontalalignment or ha {'center', 'right', 'left'}
in_layout bool
label object
linespacing float (multiple of font size)
math_fontfamily str
multialignment or ma {'left', 'right', 'center'}
parse_math bool
path_effects AbstractPathEffect
picker None or bool or float or callable
position (float, float)
rasterized bool
rotation float or {'vertical', 'horizontal'}
rotation_mode {None, 'default', 'anchor'}
sketch_params (scale: float, length: float, randomness: float)
snap bool or None
text object
transform Transform
transform_rotates_text bool
url str
usetex bool or None
verticalalignment or va {'center', 'top', 'bottom', 'baseline', 'center_baseline'}
visible bool
wrap bool
x float
y float
zorder float | |
doc_28598 |
Return divmod(value, self). | |
doc_28599 | Return True if the underlying transport supports the write_eof() method, False otherwise. |
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