Search is not available for this dataset
identifier stringlengths 1 155 | parameters stringlengths 2 6.09k | docstring stringlengths 11 63.4k | docstring_summary stringlengths 0 63.4k | function stringlengths 29 99.8k | function_tokens list | start_point list | end_point list | language stringclasses 1
value | docstring_language stringlengths 2 7 | docstring_language_predictions stringlengths 18 23 | is_langid_reliable stringclasses 2
values |
|---|---|---|---|---|---|---|---|---|---|---|---|
GeneratePassword | (length=8, include_symbols=False) | Generates a random password. | Generates a random password. | def GeneratePassword(length=8, include_symbols=False):
"""Generates a random password."""
if length < MIN_LENGTH:
raise InputError('Password length must be at least %d' % MIN_LENGTH)
candidates = (CANDIDATES_WITH_SYMBOLS if include_symbols
else CANDIDATES_WITHOUT_SYMBOLS)
categories = (CATE... | [
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_InsertAndEnsureSatisfaction | (generated, required, all_candidates) | Inserts 1 char into generated, satisfying required if not already.
If the required characters are not already in the generated string, one will
be inserted. If any required character is already in the generated string, a
random character from all_candidates will be inserted. The insertion happens
at a random l... | Inserts 1 char into generated, satisfying required if not already. | def _InsertAndEnsureSatisfaction(generated, required, all_candidates):
"""Inserts 1 char into generated, satisfying required if not already.
If the required characters are not already in the generated string, one will
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_InsertInto | (generated, candidates) | Inserts a random candidate into a random non-zero index of generated. | Inserts a random candidate into a random non-zero index of generated. | def _InsertInto(generated, candidates):
"""Inserts a random candidate into a random non-zero index of generated."""
# Avoids inserting at index 0, since the first character follows its own rule.
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GUICalculator.create_button_layout | (self) | Creates the grid of calculator buttons. | Creates the grid of calculator buttons. | def create_button_layout(self):
"Creates the grid of calculator buttons."
labels = ["exit", "mrc", "m+", "m-",
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SplitDatabase.__init__ | (self, input_file, dir_folds=None, n_splits=10, sep_read='\t', sep_write='\t', header=None,
names=None, as_binary=False, binary_col=None, write_mode='w') |
Given a database, this class is responsible for creating a training and test sets
for k folds with well-known strategies:
- k-fold cross-validation
- ShuffleSplit
Usage:
>> SplitDatabase(input_file=database, dir_folds=dir_path, n_folds=10).kfoldcrossvalidation()
... |
Given a database, this class is responsible for creating a training and test sets
for k folds with well-known strategies: | def __init__(self, input_file, dir_folds=None, n_splits=10, sep_read='\t', sep_write='\t', header=None,
names=None, as_binary=False, binary_col=None, write_mode='w'):
"""
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SplitDatabase.kfoldcrossvalidation | (self, shuffle=True, random_state=None) |
k-fold cross-validation
In k-fold cross-validation, the original sample is randomly partitioned into
k equal sized subsamples. Of the k subsamples, a single subsample is retained as
the validation data for testing the model, and the remaining k − 1 subsamples are
used as traini... |
k-fold cross-validation | def kfoldcrossvalidation(self, shuffle=True, random_state=None):
"""
k-fold cross-validation
In k-fold cross-validation, the original sample is randomly partitioned into
k equal sized subsamples. Of the k subsamples, a single subsample is retained as
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SplitDatabase.shuffle_split | (self, test_size=0.1, random_state=None) |
Shuffle Split
Random permutation cross-validator
Yields indices to split data into training and test sets.
Note: contrary to other cross-validation strategies, random splits do not guarantee that
all folds will be different, although this is still very likely for sizeable dat... |
Shuffle Split | def shuffle_split(self, test_size=0.1, random_state=None):
"""
Shuffle Split
Random permutation cross-validator
Yields indices to split data into training and test sets.
Note: contrary to other cross-validation strategies, random splits do not guarantee that
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compute_hex_hash | (s, algorithm=SIGNATURE_SHA1) |
Computes string hash using specified algorithm and return HEX string representation of hash.
:param s: String to compute hash for
:param algorithm: The name of algorithm to use for computing hash
:return: HEX string of computed hash value
|
Computes string hash using specified algorithm and return HEX string representation of hash. | def compute_hex_hash(s, algorithm=SIGNATURE_SHA1):
"""
Computes string hash using specified algorithm and return HEX string representation of hash.
:param s: String to compute hash for
:param algorithm: The name of algorithm to use for computing hash
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build_list_of_dicts | (val) |
Converts a value that can be presented as a list of dict.
In case top level item is not a list, it is wrapped with a list
Valid values examples:
- Valid dict: {"k": "v", "k2","v2"}
- List of dict: [{"k": "v"}, {"k2","v2"}]
- JSON decodable string: '{"k": "v"}', or '[{"k": "v"}]'
... |
Converts a value that can be presented as a list of dict. | def build_list_of_dicts(val):
"""
Converts a value that can be presented as a list of dict.
In case top level item is not a list, it is wrapped with a list
Valid values examples:
- Valid dict: {"k": "v", "k2","v2"}
- List of dict: [{"k": "v"}, {"k2","v2"}]
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normalize_context_value | (value) |
Escape "=" and "|" delimiter characters and json encode lists
:param value: Value to escape
:type value: int or str or list or tuple
:return: The normalized value
:rtype: str
|
Escape "=" and "|" delimiter characters and json encode lists | def normalize_context_value(value):
"""
Escape "=" and "|" delimiter characters and json encode lists
:param value: Value to escape
:type value: int or str or list or tuple
:return: The normalized value
:rtype: str
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encode_context | (context) |
Encode metadata fields based on incoming value.
List and tuple values are encoded to json strings.
:param context: dict of context to be encoded
:return: a joined string of all keys and values properly escaped and separated by a pipe character
|
Encode metadata fields based on incoming value. | def encode_context(context):
"""
Encode metadata fields based on incoming value.
List and tuple values are encoded to json strings.
:param context: dict of context to be encoded
:return: a joined string of all keys and values properly escaped and separated by a pipe character
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json_encode | (value) |
Converts value to a json encoded string
:param value: value to be encoded
:return: JSON encoded string
|
Converts value to a json encoded string | def json_encode(value):
"""
Converts value to a json encoded string
:param value: value to be encoded
:return: JSON encoded string
"""
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encode_date_to_usage_api_format | (date_obj) |
Encodes date object to `dd-mm-yyyy` format string
:param date_obj: datetime.date object to encode
:return: Encoded date as a string
|
Encodes date object to `dd-mm-yyyy` format string | def encode_date_to_usage_api_format(date_obj):
"""
Encodes date object to `dd-mm-yyyy` format string
:param date_obj: datetime.date object to encode
:return: Encoded date as a string
"""
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patch_fetch_format | (options) |
When upload type is fetch, remove the format options.
In addition, set the fetch_format options to the format value unless it was already set.
Mutates the options parameter!
:param options: URL and transformation options
|
When upload type is fetch, remove the format options.
In addition, set the fetch_format options to the format value unless it was already set.
Mutates the options parameter! | def patch_fetch_format(options):
"""
When upload type is fetch, remove the format options.
In addition, set the fetch_format options to the format value unless it was already set.
Mutates the options parameter!
:param options: URL and transformation options
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chain_transformations | (options, transformations) |
Helper function, allows chaining transformations to the end of transformations list
The result of this function is an updated options parameter
:param options: Original options
:param transformations: Transformations to chain at the end
:return: Resulting options
|
Helper function, allows chaining transformations to the end of transformations list | def chain_transformations(options, transformations):
"""
Helper function, allows chaining transformations to the end of transformations list
The result of this function is an updated options parameter
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:param transformations: Transformations to chain at the ... | [
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unsigned_download_url_prefix | (source, cloud_name, private_cdn, cdn_subdomain,
secure_cdn_subdomain, cname, secure, secure_distribution) | cdn_subdomain and secure_cdn_subdomain
1) Customers in shared distribution (e.g. res.cloudinary.com)
if cdn_domain is true uses res-[1-5].cloudinary.com for both http and https.
Setting secure_cdn_subdomain to false disables this for https.
2) Customers with private cdn
if cdn_domain is true u... | cdn_subdomain and secure_cdn_subdomain
1) Customers in shared distribution (e.g. res.cloudinary.com)
if cdn_domain is true uses res-[1-5].cloudinary.com for both http and https.
Setting secure_cdn_subdomain to false disables this for https.
2) Customers with private cdn
if cdn_domain is true u... | def unsigned_download_url_prefix(source, cloud_name, private_cdn, cdn_subdomain,
secure_cdn_subdomain, cname, secure, secure_distribution):
"""cdn_subdomain and secure_cdn_subdomain
1) Customers in shared distribution (e.g. res.cloudinary.com)
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cloudinary_scaled_url | (source, width, transformation, options) |
Generates a cloudinary url scaled to specified width.
:param source: The resource
:param width: Width in pixels of the srcset item
:param transformation: Custom transformation that overrides transformations provided in options
:param options: A dict with additional opti... |
Generates a cloudinary url scaled to specified width. | def cloudinary_scaled_url(source, width, transformation, options):
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Generates a cloudinary url scaled to specified width.
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smart_escape | (source, unsafe=r"([^a-zA-Z0-9_.\-\/:]+)") |
Based on ruby's CGI::unescape. In addition does not escape / :
:param source: Source string to escape
:param unsafe: Unsafe characters
:return: Escaped string
|
Based on ruby's CGI::unescape. In addition does not escape / : | def smart_escape(source, unsafe=r"([^a-zA-Z0-9_.\-\/:]+)"):
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Based on ruby's CGI::unescape. In addition does not escape / :
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download_folder | (folder_path, **options) |
Creates and returns a URL that when invoked creates an archive of a folder.
:param folder_path: The full path from the root that is used to generate download url.
:type folder_path: str
:param options: Additional options.
:type options: dict, optional
:return: Signed URL to... |
Creates and returns a URL that when invoked creates an archive of a folder.
:param folder_path: The full path from the root that is used to generate download url.
:type folder_path: str
:param options: Additional options.
:type options: dict, optional
:return: Signed URL to... | def download_folder(folder_path, **options):
"""
Creates and returns a URL that when invoked creates an archive of a folder.
:param folder_path: The full path from the root that is used to generate download url.
:type folder_path: str
:param options: Additional options.
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download_backedup_asset | (asset_id, version_id, **options) |
The returned url allows downloading the backedup asset based on the the asset ID and the version ID.
Parameters asset_id and version_id are returned with api.resource(<PUBLIC_ID1>, versions=True) API call.
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"""
The returned url allows downloading the backedup asset based on the the asset ID and the version ID.
Parameters asset_id and version_id are returned with api.resource(<PUBLIC_ID1>, versions=True) API call.
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build_single_eager | (options) |
Builds a single eager transformation which consists of transformation and (optionally) format joined by "/"
:param options: Options containing transformation parameters and (optionally) a "format" key
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The latter l... |
Builds a single eager transformation which consists of transformation and (optionally) format joined by "/" | def build_single_eager(options):
"""
Builds a single eager transformation which consists of transformation and (optionally) format joined by "/"
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build_multi_and_sprite_params | (**options) |
Build params for multi, download_multi, generate_sprite, and download_generated_sprite methods
|
Build params for multi, download_multi, generate_sprite, and download_generated_sprite methods
| def build_multi_and_sprite_params(**options):
"""
Build params for multi, download_multi, generate_sprite, and download_generated_sprite methods
"""
tag = options.get("tag")
urls = options.get("urls")
if bool(tag) == bool(urls):
raise ValueError("Either 'tag' or 'urls' parameter has to b... | [
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process_fps | (fps) |
Serializes fps transformation parameter
:param fps: A single number, a list of mixed type, a string, including open-ended and closed range values
Examples: '24-29.97', 24, 24.973, '-24', [24, 29.97]
:return: string
|
Serializes fps transformation parameter | def process_fps(fps):
"""
Serializes fps transformation parameter
:param fps: A single number, a list of mixed type, a string, including open-ended and closed range values
Examples: '24-29.97', 24, 24.973, '-24', [24, 29.97]
:return: string
"""
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process_ki | (ki) |
Serializes keyframe_interval parameter
:param ki: Keyframe interval. Should be either a string or a positive real number.
:return: string
|
Serializes keyframe_interval parameter
:param ki: Keyframe interval. Should be either a string or a positive real number.
:return: string
| def process_ki(ki):
"""
Serializes keyframe_interval parameter
:param ki: Keyframe interval. Should be either a string or a positive real number.
:return: string
"""
if ki is None:
return None
if isinstance(ki, string_types):
return ki
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base64_encode_url | (url) |
Returns the Base64-decoded version of url.
The method tries to unquote the url because quoting it
:param str url:
the url to encode. the value is URIdecoded and then
re-encoded before converting to base64 representation
|
Returns the Base64-decoded version of url.
The method tries to unquote the url because quoting it | def base64_encode_url(url):
"""
Returns the Base64-decoded version of url.
The method tries to unquote the url because quoting it
:param str url:
the url to encode. the value is URIdecoded and then
re-encoded before converting to base64 representation
"""
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base64url_encode | (data) |
Url safe version of urlsafe_b64encode with stripped `=` sign at the end.
:param data: input data
:return: Base64 URL safe encoded string
|
Url safe version of urlsafe_b64encode with stripped `=` sign at the end. | def base64url_encode(data):
"""
Url safe version of urlsafe_b64encode with stripped `=` sign at the end.
:param data: input data
:return: Base64 URL safe encoded string
"""
return to_string(base64.urlsafe_b64encode(to_bytes(data))) | [
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encode_unicode_url | (url_str) |
Quote and encode possible unicode url string (applicable for python2)
:param url_str: Url string to encode
:return: Encoded string
|
Quote and encode possible unicode url string (applicable for python2) | def encode_unicode_url(url_str):
"""
Quote and encode possible unicode url string (applicable for python2)
:param url_str: Url string to encode
:return: Encoded string
"""
if six.PY2:
url_str = urllib.quote(url_str.encode('utf-8'), ":/?#[]@!$&'()*+,;=")
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__json_serializer | (obj) | JSON serializer for objects not serializable by default json code | JSON serializer for objects not serializable by default json code | def __json_serializer(obj):
"""JSON serializer for objects not serializable by default json code"""
if isinstance(obj, (datetime, date)):
return obj.isoformat()
raise TypeError("Object of type %s is not JSON serializable" % type(obj)) | [
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is_remote_url | (file) | Basic URL scheme check to define if it's remote URL | Basic URL scheme check to define if it's remote URL | def is_remote_url(file):
"""Basic URL scheme check to define if it's remote URL"""
return isinstance(file, string_types) and re.match(REMOTE_URL_RE, file) | [
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file_io_size | (file_io) |
Helper function for getting file-like object size(suitable for both files and streams)
:param file_io: io.IOBase
:return: size
|
Helper function for getting file-like object size(suitable for both files and streams) | def file_io_size(file_io):
"""
Helper function for getting file-like object size(suitable for both files and streams)
:param file_io: io.IOBase
:return: size
"""
initial_position = file_io.tell()
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check_property_enabled | (f) |
Used as a class method decorator to check whether class is enabled(self.enabled is True)
:param f: function to call
:return: None if not enabled, otherwise calls function f
|
Used as a class method decorator to check whether class is enabled(self.enabled is True) | def check_property_enabled(f):
"""
Used as a class method decorator to check whether class is enabled(self.enabled is True)
:param f: function to call
:return: None if not enabled, otherwise calls function f
"""
def wrapper(*args, **kwargs):
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verify_api_response_signature | (public_id, version, signature, algorithm=None) |
Verifies the authenticity of an API response signature
:param public_id: The public id of the asset as returned in the API response
:param version: The version of the asset as returned in the API response
:param signature: Actual signature. Can be retrieved from the X-Cld-Signature header
:param... |
Verifies the authenticity of an API response signature | def verify_api_response_signature(public_id, version, signature, algorithm=None):
"""
Verifies the authenticity of an API response signature
:param public_id: The public id of the asset as returned in the API response
:param version: The version of the asset as returned in the API response
:param... | [
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verify_notification_signature | (body, timestamp, signature, valid_for=7200, algorithm=None) |
Verifies the authenticity of a notification signature
:param body: Json of the request's body
:param timestamp: Unix timestamp. Can be retrieved from the X-Cld-Timestamp header
:param signature: Actual signature. Can be retrieved from the X-Cld-Signature header
:param valid_for: The desired time i... |
Verifies the authenticity of a notification signature | def verify_notification_signature(body, timestamp, signature, valid_for=7200, algorithm=None):
"""
Verifies the authenticity of a notification signature
:param body: Json of the request's body
:param timestamp: Unix timestamp. Can be retrieved from the X-Cld-Timestamp header
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get_http_connector | (conf, options) |
Used to create http connector, depends on api_proxy configuration parameter
:param conf: configuration object
:param options: additional options
:return: ProxyManager if api_proxy is set, otherwise PoolManager object
|
Used to create http connector, depends on api_proxy configuration parameter | def get_http_connector(conf, options):
"""
Used to create http connector, depends on api_proxy configuration parameter
:param conf: configuration object
:param options: additional options
:return: ProxyManager if api_proxy is set, otherwise PoolManager object
"""
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safe_cast | (val, casting_fn, default=None) |
Attempts to cast a value to another using a given casting function
Will return a default value if casting fails (configurable, defaults to None)
:param val: The value to cast
:param casting_fn: The casting function that will receive the value to cast
:param default: The return value if casting fai... |
Attempts to cast a value to another using a given casting function
Will return a default value if casting fails (configurable, defaults to None) | def safe_cast(val, casting_fn, default=None):
"""
Attempts to cast a value to another using a given casting function
Will return a default value if casting fails (configurable, defaults to None)
:param val: The value to cast
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compute_power_of_solutions | (template_eval, template_tasks, tier) | Compute power for each solution in eval stats.
Solution power is how many tasks an action solves.
| Compute power for each solution in eval stats. | def compute_power_of_solutions(template_eval, template_tasks, tier):
"""Compute power for each solution in eval stats.
Solution power is how many tasks an action solves.
"""
template_tasks = set(template_tasks)
actions_on_tasks = template_eval['solution_power'][tier]['actions_on_tasks']
task_id... | [
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RequirementTracker.add | (self, req: InstallRequirement) | Add an InstallRequirement to build tracking.
| Add an InstallRequirement to build tracking.
| def add(self, req: InstallRequirement) -> None:
"""Add an InstallRequirement to build tracking.
"""
assert req.link
# Get the file to write information about this requirement.
entry_path = self._entry_path(req.link)
# Try reading from the file. If it exists and can be r... | [
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RequirementTracker.remove | (self, req: InstallRequirement) | Remove an InstallRequirement from build tracking.
| Remove an InstallRequirement from build tracking.
| def remove(self, req: InstallRequirement) -> None:
"""Remove an InstallRequirement from build tracking.
"""
assert req.link
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os.unlink(self._entry_path(req.link))
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identity_block | (input_tensor, kernel_size, filters, stage, block) | The identity block is the block that has no conv layer at shortcut.
# Arguments
input_tensor: input tensor
kernel_size: default 3, the kernel size of
middle conv layer at main path
filters: list of integers, the filters of 3 conv layer at main path
stage: integer, current stage label... | The identity block is the block that has no conv layer at shortcut.
# Arguments
input_tensor: input tensor
kernel_size: default 3, the kernel size of
middle conv layer at main path
filters: list of integers, the filters of 3 conv layer at main path
stage: integer, current stage label... | def identity_block(input_tensor, kernel_size, filters, stage, block):
"""The identity block is the block that has no conv layer at shortcut.
# Arguments
input_tensor: input tensor
kernel_size: default 3, the kernel size of
middle conv layer at main path
filters: list of integers, the fil... | [
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conv_block | (input_tensor,
kernel_size,
filters,
stage,
block,
strides=(2, 2)) | A block that has a conv layer at shortcut.
# Arguments
input_tensor: input tensor
kernel_size: default 3, the kernel size of
middle conv layer at main path
filters: list of integers, the filters of 3 conv layer at main path
stage: integer, current stage label, used for generating lay... | A block that has a conv layer at shortcut.
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input_tensor: input tensor
kernel_size: default 3, the kernel size of
middle conv layer at main path
filters: list of integers, the filters of 3 conv layer at main path
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filters,
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# Arguments
input_tensor: input tensor
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register_handler | (handler) |
Install application-specific WMF image handler.
:param handler: Handler object.
|
Install application-specific WMF image handler. | def register_handler(handler):
"""
Install application-specific WMF image handler.
:param handler: Handler object.
"""
global _handler
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LazySettings._setup | (self, name=None) |
Load the settings module pointed to by the environment variable. This
is used the first time settings are needed, if the user hasn't
configured settings manually.
|
Load the settings module pointed to by the environment variable. This
is used the first time settings are needed, if the user hasn't
configured settings manually.
| def _setup(self, name=None):
"""
Load the settings module pointed to by the environment variable. This
is used the first time settings are needed, if the user hasn't
configured settings manually.
"""
settings_module = os.environ.get(ENVIRONMENT_VARIABLE)
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LazySettings.__getattr__ | (self, name) | Return the value of a setting and cache it in self.__dict__. | Return the value of a setting and cache it in self.__dict__. | def __getattr__(self, name):
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LazySettings.__setattr__ | (self, name, value) |
Set the value of setting. Clear all cached values if _wrapped changes
(@override_settings does this) or clear single values when set.
|
Set the value of setting. Clear all cached values if _wrapped changes
( | def __setattr__(self, name, value):
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Set the value of setting. Clear all cached values if _wrapped changes
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LazySettings.__delattr__ | (self, name) | Delete a setting and clear it from cache if needed. | Delete a setting and clear it from cache if needed. | def __delattr__(self, name):
"""Delete a setting and clear it from cache if needed."""
super().__delattr__(name)
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LazySettings.configure | (self, default_settings=global_settings, **options) |
Called to manually configure the settings. The 'default_settings'
parameter sets where to retrieve any unspecified values from (its
argument must support attribute access (__getattr__)).
|
Called to manually configure the settings. The 'default_settings'
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LazySettings._add_script_prefix | (value) |
Add SCRIPT_NAME prefix to relative paths.
Useful when the app is being served at a subpath and manually prefixing
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|
Add SCRIPT_NAME prefix to relative paths. | def _add_script_prefix(value):
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LazySettings.configured | (self) | Return True if the settings have already been configured. | Return True if the settings have already been configured. | def configured(self):
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UserSettingsHolder.__init__ | (self, default_settings) |
Requests for configuration variables not in this class are satisfied
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|
Requests for configuration variables not in this class are satisfied
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Requests for configuration variables not in this class are satisfied
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HealpyModel.__init__ | (self, network, input_shape=None, optimizer=None, save_dir=None,
restore_point=None, summary_dir=None, init_step=0, is_chief=True) |
Initializes a base model
:param network: The underlying network of the model (expected to be either a tf.keras.Sequential or a subclass
of it)
:param input_shape: Optional input shape of the network, necessary if one wants to restore the model
:param optimizer: O... |
Initializes a base model
:param network: The underlying network of the model (expected to be either a tf.keras.Sequential or a subclass
of it)
:param input_shape: Optional input shape of the network, necessary if one wants to restore the model
:param optimizer: O... | def __init__(self, network, input_shape=None, optimizer=None, save_dir=None,
restore_point=None, summary_dir=None, init_step=0, is_chief=True):
"""
Initializes a base model
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HealpyModel.clean_summaries | (self, force=False) |
Removes redundant summary directories...
:param force: force the removal even if the worker is not chief
|
Removes redundant summary directories...
:param force: force the removal even if the worker is not chief
| def clean_summaries(self, force=False):
"""
Removes redundant summary directories...
:param force: force the removal even if the worker is not chief
"""
if self.is_chief or force:
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num_workers = hvd.size()
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... | [
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HealpyModel.update_step | (self) |
increments the train step of the model by 1
|
increments the train step of the model by 1
| def update_step(self):
"""
increments the train step of the model by 1
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HealpyModel.set_step | (self, step) |
Sets the current training step of the model to a given value
:param step: The new step (int)
|
Sets the current training step of the model to a given value
:param step: The new step (int)
| def set_step(self, step):
"""
Sets the current training step of the model to a given value
:param step: The new step (int)
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HealpyModel.restore_model | (self, restore_point=None) |
Restores the weights of the network given a restore point
:param restore_point: either a directory that includes checkpoints (of which the most recent will be chosen)
or the path to a specific checkpoint to restore from, default to value at init of model
|
Restores the weights of the network given a restore point
:param restore_point: either a directory that includes checkpoints (of which the most recent will be chosen)
or the path to a specific checkpoint to restore from, default to value at init of model
| def restore_model(self, restore_point=None):
"""
Restores the weights of the network given a restore point
:param restore_point: either a directory that includes checkpoints (of which the most recent will be chosen)
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HealpyModel.save_model | (self, save_dir=None, force=False) |
Saves the weights of the model into a given directory, this function won't do anything if the model is not chief
:param save_dir: the path where to save the weights, defaults to the value at init of model
:param force: write the checkpoint even if the model is not chief, this can lead to errors... |
Saves the weights of the model into a given directory, this function won't do anything if the model is not chief
:param save_dir: the path where to save the weights, defaults to the value at init of model
:param force: write the checkpoint even if the model is not chief, this can lead to errors... | def save_model(self, save_dir=None, force=False):
"""
Saves the weights of the model into a given directory, this function won't do anything if the model is not chief
:param save_dir: the path where to save the weights, defaults to the value at init of model
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HealpyModel.build_network | (self, input_shape) |
Builds the internal HealpyGCNN with a given input shape
:param input_shape: input shape of the netork
|
Builds the internal HealpyGCNN with a given input shape
:param input_shape: input shape of the netork
| def build_network(self, input_shape):
"""
Builds the internal HealpyGCNN with a given input shape
:param input_shape: input shape of the netork
"""
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HealpyModel.print_summary | (self, **kwargs) |
Prints the summary of the internal network
:param kwargs: passed to HealpyGCNN.summary
|
Prints the summary of the internal network
:param kwargs: passed to HealpyGCNN.summary
| def print_summary(self, **kwargs):
"""
Prints the summary of the internal network
:param kwargs: passed to HealpyGCNN.summary
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HealpyModel.base_train_step | (self, input_tensor, loss_function, input_labels=None, clip_by_value=None, clip_by_norm=None,
clip_by_global_norm=None, training=True, num_workers=None, train_indices=None,
return_loss=False) |
A base train step given a loss funtion and an input tensor it evaluates the network and performs a single
gradient decent step, if multiple clippings are requested the order will be:
* by value
* by norm
* by global norm
:param input_tensor: The input of the ... |
A base train step given a loss funtion and an input tensor it evaluates the network and performs a single
gradient decent step, if multiple clippings are requested the order will be:
* by value
* by norm
* by global norm
:param input_tensor: The input of the ... | def base_train_step(self, input_tensor, loss_function, input_labels=None, clip_by_value=None, clip_by_norm=None,
clip_by_global_norm=None, training=True, num_workers=None, train_indices=None,
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"""
A base train step given a loss funtion a... | [
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HealpyModel.setup_delta_loss_step | (self, batch_size, off_sets, n_points=1, n_channels=1, n_output=None, jac_weight=0.0,
force_params=None, force_weight=1.0, jac_cond_weight=None, use_log_det=True,
no_correlations=False, tikhonov_regu=False, weights=None, eps=1e-32, n_partial=None,
... |
This sets up a function that performs one training step with the delta loss, which tries to maximize the
information of the summary statistics. Note it needs the maps need to be ordered in a specific way:
* The shape of the maps is (n_points*n_same*(2*n_params+1), len(indices), n_channels)... |
This sets up a function that performs one training step with the delta loss, which tries to maximize the
information of the summary statistics. Note it needs the maps need to be ordered in a specific way:
* The shape of the maps is (n_points*n_same*(2*n_params+1), len(indices), n_channels)... | def setup_delta_loss_step(self, batch_size, off_sets, n_points=1, n_channels=1, n_output=None, jac_weight=0.0,
force_params=None, force_weight=1.0, jac_cond_weight=None, use_log_det=True,
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HealpyModel.broadcast_variables | (self) |
boradcasts the variables from the chief to all other workers from the network and optimizer
|
boradcasts the variables from the chief to all other workers from the network and optimizer
| def broadcast_variables(self):
"""
boradcasts the variables from the chief to all other workers from the network and optimizer
"""
hvd.broadcast_variables(self.network.weights, root_rank=0)
hvd.broadcast_variables(self.optimizer.variables(), root_rank=0) | [
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HealpyModel.setup_1st_order_estimator | (self, dset, fidu_param, off_sets, print_params=False, tf_dtype=tf.float32,
tikohnov=0.0, layer=None, dset_is_sims=False) |
Sets up a first order estimator from a given dataset that will be evaluated
:param dset: The dataset that will be evaluated
:param fidu_param: the fiducial parameter of the estimator
:param off_sets: the offsets used for the perturbations
:param print_params: print the calculate... |
Sets up a first order estimator from a given dataset that will be evaluated
:param dset: The dataset that will be evaluated
:param fidu_param: the fiducial parameter of the estimator
:param off_sets: the offsets used for the perturbations
:param print_params: print the calculate... | def setup_1st_order_estimator(self, dset, fidu_param, off_sets, print_params=False, tf_dtype=tf.float32,
tikohnov=0.0, layer=None, dset_is_sims=False):
"""
Sets up a first order estimator from a given dataset that will be evaluated
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HealpyModel.estimate | (self, input_tensor) |
Calculates the first order estimates of the underlying model parameter given a network input
:param input_tensor: The input to feed in the network
:return: The parameter estimates
|
Calculates the first order estimates of the underlying model parameter given a network input
:param input_tensor: The input to feed in the network
:return: The parameter estimates
| def estimate(self, input_tensor):
"""
Calculates the first order estimates of the underlying model parameter given a network input
:param input_tensor: The input to feed in the network
:return: The parameter estimates
"""
if self.estimator is None:
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HealpyModel.__call__ | (self, input_tensor, training=False, numpy=False, layer=None, *args, **kwargs) |
Calls the underlying network
:param input_tensor: the tensor (or array) to call on
:param training: whether we are training or evaluating (e.g. necessary gor batch norm)
:param args: additional arguments passed to the network
:param kwargs: additional keyword arguments passed to... |
Calls the underlying network
:param input_tensor: the tensor (or array) to call on
:param training: whether we are training or evaluating (e.g. necessary gor batch norm)
:param args: additional arguments passed to the network
:param kwargs: additional keyword arguments passed to... | def __call__(self, input_tensor, training=False, numpy=False, layer=None, *args, **kwargs):
"""
Calls the underlying network
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:param training: whether we are training or evaluating (e.g. necessary gor batch norm)
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UserNSVD1.__init__ | (self, train_file=None, test_file=None, metadata_file=None, output_file=None, epochs=30,
learn_rate=0.01, delta=0.015, factors=10, init_mean=0, init_stdev=0.1, stop_criteria=0.001,
batch=False, n2=10, learn_rate2=0.01, delta2=0.015, sep='\t', output_sep='\t', metadata_sep='\t',
... |
UserNSVD1 for rating prediction
Usage::
>> UserNSVD1(train, test, metadata_file='user_metadata.dat').compute()
>> UserNSVD1(train, test, metadata_file='user_metadata.dat', batch=True).compute()
:param train_file: File which contains the train set. This file needs to h... |
UserNSVD1 for rating prediction | def __init__(self, train_file=None, test_file=None, metadata_file=None, output_file=None, epochs=30,
learn_rate=0.01, delta=0.015, factors=10, init_mean=0, init_stdev=0.1, stop_criteria=0.001,
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UserNSVD1.init_model | (self) |
Method to treat and initialize the model. Extends init_model from BaseNSVD1
|
Method to treat and initialize the model. Extends init_model from BaseNSVD1 | def init_model(self):
"""
Method to treat and initialize the model. Extends init_model from BaseNSVD1
"""
super(UserNSVD1, self).init_model()
self.non_zero_x = []
self.d = []
self.metadata = ReadFile(self.metadata_file, sep=self.metadata_sep, as_binary=self.me... | [
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UserNSVD1.fit | (self) |
This method performs iterations of stochastic gradient ascent over the training data.
|
This method performs iterations of stochastic gradient ascent over the training data. | def fit(self):
"""
This method performs iterations of stochastic gradient ascent over the training data.
"""
for k in range(self.epochs):
rmse = 0
count_error = 0
if self.batch:
self.p = np.dot(self.x, self.w)
for u... | [
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UserNSVD1.update_factors | (self, user, u) |
Update latent factors according to the stochastic gradient descent update rule
:param user: User
:type user: int
:param u: User ID from self.users
:type u: int
:return: error and count
|
Update latent factors according to the stochastic gradient descent update rule | def update_factors(self, user, u):
"""
Update latent factors according to the stochastic gradient descent update rule
:param user: User
:type user: int
:param u: User ID from self.users
:type u: int
:return: error and count
"""
c, e = 0, 0
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UserNSVD1.compute | (self, verbose=True, metrics=None, verbose_evaluation=True, as_table=False, table_sep='\t') |
Extends compute method from BaseRatingPrediction. Method to run recommender algorithm
:param verbose: Print recommender and database information
:type verbose: bool, default True
:param metrics: List of evaluation measures
:type metrics: list, default None
:param verb... |
Extends compute method from BaseRatingPrediction. Method to run recommender algorithm | def compute(self, verbose=True, metrics=None, verbose_evaluation=True, as_table=False, table_sep='\t'):
"""
Extends compute method from BaseRatingPrediction. Method to run recommender algorithm
:param verbose: Print recommender and database information
:type verbose: bool, default True
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newer_pairwise_group | (sources_groups, targets) | Walk both arguments in parallel, testing if each source group is newer
than its corresponding target. Returns a pair of lists (sources_groups,
targets) where sources is newer than target, according to the semantics
of 'newer_group()'.
| Walk both arguments in parallel, testing if each source group is newer
than its corresponding target. Returns a pair of lists (sources_groups,
targets) where sources is newer than target, according to the semantics
of 'newer_group()'.
| def newer_pairwise_group(sources_groups, targets):
"""Walk both arguments in parallel, testing if each source group is newer
than its corresponding target. Returns a pair of lists (sources_groups,
targets) where sources is newer than target, according to the semantics
of 'newer_group()'.
"""
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_parse_arguments | (argv) | Parses command-line arguments. | Parses command-line arguments. | def _parse_arguments(argv):
"""Parses command-line arguments."""
parser = argparse.ArgumentParser()
parser.add_argument(
'--epochs',
help='The number of epochs to train',
type=int, default=5)
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main | () | Parses command line arguments and kicks off model training. | Parses command line arguments and kicks off model training. | def main():
"""Parses command line arguments and kicks off model training."""
args = _parse_arguments(sys.argv[1:])[0]
# TODO: define a TPU strategy
resolver = tf.distribute.cluster_resolver.TPUClusterResolver(
tpu=args.tpu_address)
tf.config.experimental_connect_to_cluster(resolver)
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_have_cython | () |
Return True if Cython can be imported.
|
Return True if Cython can be imported.
| def _have_cython():
"""
Return True if Cython can be imported.
"""
cython_impl = 'Cython.Distutils.build_ext'
try:
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__import__(cython_impl, fromlist=['build_ext']).build_ext
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Extension._convert_pyx_sources_to_lang | (self) |
Replace sources with .pyx extensions to sources with the target
language extension. This mechanism allows language authors to supply
pre-converted sources but to prefer the .pyx sources.
|
Replace sources with .pyx extensions to sources with the target
language extension. This mechanism allows language authors to supply
pre-converted sources but to prefer the .pyx sources.
| def _convert_pyx_sources_to_lang(self):
"""
Replace sources with .pyx extensions to sources with the target
language extension. This mechanism allows language authors to supply
pre-converted sources but to prefer the .pyx sources.
"""
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CrossValidation.__init__ | (self, input_file, recommender, dir_folds, k_folds=10, header=None, sep='\t', write_predictions=False,
write_sep='\t', recommender_verbose=False, evaluation_in_fold_verbose=True, metrics=None,
as_table=False, table_sep='\t', del_folds=False, random_seed=None) |
Cross Validation
This strategy is responsible to divide the database in K folds, in which each fold contain a train and a test
set. Its also responsible to run and evaluate the recommender results in each fold and calculate the mean and
the standard deviation.
Usage:
... |
Cross Validation | def __init__(self, input_file, recommender, dir_folds, k_folds=10, header=None, sep='\t', write_predictions=False,
write_sep='\t', recommender_verbose=False, evaluation_in_fold_verbose=True, metrics=None,
as_table=False, table_sep='\t', del_folds=False, random_seed=None):
"""
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CrossValidation.generate_folds | (self) |
Method to generate folds with k fold cross validation
|
Method to generate folds with k fold cross validation | def generate_folds(self):
"""
Method to generate folds with k fold cross validation
"""
SplitDatabase(input_file=self.input_file, n_splits=self.k_folds, dir_folds=self.dir_folds,
sep_read=self.sep, header=self.header).kfoldcrossvalidation(random_state=self.random_... | [
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CrossValidation.execute_algorithm | (self) |
Method to run recommender algorithm in k folds
|
Method to run recommender algorithm in k folds | def execute_algorithm(self):
"""
Method to run recommender algorithm in k folds
"""
for k in range(self.k_folds):
train_file = self.dir_folds + 'folds/%d/train.dat' % k
test_file = self.dir_folds + 'folds/%d/test.dat' % k
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CrossValidation.evaluate | (self, verbose=True) |
Method to evaluate folds results and generate mean and standard deviation
:param verbose: If True, print evaluation results
:type verbose: bool, default True
|
Method to evaluate folds results and generate mean and standard deviation | def evaluate(self, verbose=True):
"""
Method to evaluate folds results and generate mean and standard deviation
:param verbose: If True, print evaluation results
:type verbose: bool, default True
"""
mean_dict = defaultdict(dict)
std_dict = defaultdict(dict)
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CrossValidation.erase_folds | (self) |
Method to delete folds after evaluation
|
Method to delete folds after evaluation | def erase_folds(self):
"""
Method to delete folds after evaluation
"""
folds = self.dir_folds + 'folds/'
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CrossValidation.compute | (self, verbose=True) |
Method to run the cross validation
:param verbose: If True, print header
:type verbose: bool, default True
|
Method to run the cross validation | def compute(self, verbose=True):
"""
Method to run the cross validation
:param verbose: If True, print header
:type verbose: bool, default True
"""
if verbose:
print("[Case Recommender: Cross Validation]\n")
print("Database:: %s \nRecommender A... | [
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Greatest.as_sqlite | (self, compiler, connection, **extra_context) | Use the MAX function on SQLite. | Use the MAX function on SQLite. | def as_sqlite(self, compiler, connection, **extra_context):
"""Use the MAX function on SQLite."""
return super().as_sqlite(compiler, connection, function='MAX', **extra_context) | [
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Least.as_sqlite | (self, compiler, connection, **extra_context) | Use the MIN function on SQLite. | Use the MIN function on SQLite. | def as_sqlite(self, compiler, connection, **extra_context):
"""Use the MIN function on SQLite."""
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UserKNN.__init__ | (self, train_file=None, test_file=None, output_file=None, similarity_metric="cosine", k_neighbors=None,
as_similar_first=False, sep='\t', output_sep='\t') |
UserKNN for rating prediction
This algorithm predicts ratings for each user based on the similar items that his neighbors
(similar users) consumed.
Usage::
>> UserKNN(train, test).compute()
>> UserKNN(train, test, ranking_file, as_similar_first=True, k_neighbo... |
UserKNN for rating prediction | def __init__(self, train_file=None, test_file=None, output_file=None, similarity_metric="cosine", k_neighbors=None,
as_similar_first=False, sep='\t', output_sep='\t'):
"""
UserKNN for rating prediction
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UserKNN.init_model | (self) |
Method to initialize the model. Compute similarity matrix based on user (user x user)
|
Method to initialize the model. Compute similarity matrix based on user (user x user) | def init_model(self):
"""
Method to initialize the model. Compute similarity matrix based on user (user x user)
"""
super(UserKNN, self).init_model()
self.users_id_viewed_item = {}
# Set the value for k
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UserKNN.predict | (self) |
Method to predict ratings for all known users in the train set.
|
Method to predict ratings for all known users in the train set. | def predict(self):
"""
Method to predict ratings for all known users in the train set.
"""
for user in self.users:
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UserKNN.predict_scores | (self, user, unpredicted_items) |
In this implementation, for each unknown item,
which will be predicted, we first look for users that seen that item and calculate the similarity between them
and the user. Then we sort these similarities and get the most similar k's. Finally, the score of the
unknown item will be the su... |
In this implementation, for each unknown item,
which will be predicted, we first look for users that seen that item and calculate the similarity between them
and the user. Then we sort these similarities and get the most similar k's. Finally, the score of the
unknown item will be the su... | def predict_scores(self, user, unpredicted_items):
"""
In this implementation, for each unknown item,
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and the user. Then we sort these similarities and get the most similar k's.... | [
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UserKNN.predict_similar_first_scores | (self, user, unpredicted_items) |
In this implementation, for each unknown item, which will be
predicted, we first look for its k most similar users and then take the intersection with the users that
seen that item. Finally, the score of the unknown item will be the sum of the similarities.
rui = bui + (sum((rvi - bvi... |
In this implementation, for each unknown item, which will be
predicted, we first look for its k most similar users and then take the intersection with the users that
seen that item. Finally, the score of the unknown item will be the sum of the similarities. | def predict_similar_first_scores(self, user, unpredicted_items):
"""
In this implementation, for each unknown item, which will be
predicted, we first look for its k most similar users and then take the intersection with the users that
seen that item. Finally, the score of the unknown ite... | [
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UserKNN.compute | (self, verbose=True, metrics=None, verbose_evaluation=True, as_table=False, table_sep='\t') |
Extends compute method from BaseItemRecommendation. Method to run recommender algorithm
:param verbose: Print recommender and database information
:type verbose: bool, default True
:param metrics: List of evaluation metrics
:type metrics: list, default None
:param ver... |
Extends compute method from BaseItemRecommendation. Method to run recommender algorithm | def compute(self, verbose=True, metrics=None, verbose_evaluation=True, as_table=False, table_sep='\t'):
"""
Extends compute method from BaseItemRecommendation. Method to run recommender algorithm
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get_all_headers | (message, key) |
Given an HTTPMessage, return all headers matching a given key.
|
Given an HTTPMessage, return all headers matching a given key.
| def get_all_headers(message, key):
"""
Given an HTTPMessage, return all headers matching a given key.
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_convert_vcf_to_table | (vcf_filename: Path) | Converts all records in a vcf file into a list of dictionaries. | Converts all records in a vcf file into a list of dictionaries. | def _convert_vcf_to_table(vcf_filename: Path) -> List[Dict[str, Any]]:
"""Converts all records in a vcf file into a list of dictionaries."""
table: List[Dict[str, str]] = list()
seen_positions = set()
with vcf_filename.open('r') as file1:
vcf_reader = vcf.Reader(file1)
for record in vcf_reader:
data = _conve... | [
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parse_vcf_file | (filename: Path, set_index: bool = True) |
Converts the VCF file generated by breseq into a pandas Dataframe.
Parameters
----------
filename: Path
Either a folder containing a single breseq run or a path to the vcf file itself.
set_index:bool; default True
Whether to set the index of the dataframe.
Returns
-------
pandas.DataFrame
- Index -> (VC... |
Converts the VCF file generated by breseq into a pandas Dataframe.
Parameters
----------
filename: Path
Either a folder containing a single breseq run or a path to the vcf file itself.
set_index:bool; default True
Whether to set the index of the dataframe.
Returns
-------
pandas.DataFrame
- Index -> (VC... | def parse_vcf_file(filename: Path, set_index: bool = True) -> pandas.DataFrame:
"""
Converts the VCF file generated by breseq into a pandas Dataframe.
Parameters
----------
filename: Path
Either a folder containing a single breseq run or a path to the vcf file itself.
set_index:bool; default True
Whether to ... | [
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split_unquoted_newlines | (stmt) | Split a string on all unquoted newlines.
Unlike str.splitlines(), this will ignore CR/LF/CR+LF if the requisite
character is inside of a string. | Split a string on all unquoted newlines. | def split_unquoted_newlines(stmt):
"""Split a string on all unquoted newlines.
Unlike str.splitlines(), this will ignore CR/LF/CR+LF if the requisite
character is inside of a string."""
text = str(stmt)
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remove_quotes | (val) | Helper that removes surrounding quotes from strings. | Helper that removes surrounding quotes from strings. | def remove_quotes(val):
"""Helper that removes surrounding quotes from strings."""
if val is None:
return
if val[0] in ('"', "'") and val[0] == val[-1]:
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recurse | (*cls) | Function decorator to help with recursion
:param cls: Classes to not recurse over
:return: function
| Function decorator to help with recursion | def recurse(*cls):
"""Function decorator to help with recursion
:param cls: Classes to not recurse over
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"""
def wrap(f):
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imt | (token, i=None, m=None, t=None) | Helper function to simplify comparisons Instance, Match and TokenType
:param token:
:param i: Class or Tuple/List of Classes
:param m: Tuple of TokenType & Value. Can be list of Tuple for multiple
:param t: TokenType or Tuple/List of TokenTypes
:return: bool
| Helper function to simplify comparisons Instance, Match and TokenType
:param token:
:param i: Class or Tuple/List of Classes
:param m: Tuple of TokenType & Value. Can be list of Tuple for multiple
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| def imt(token, i=None, m=None, t=None):
"""Helper function to simplify comparisons Instance, Match and TokenType
:param token:
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consume | (iterator, n) | Advance the iterator n-steps ahead. If n is none, consume entirely. | Advance the iterator n-steps ahead. If n is none, consume entirely. | def consume(iterator, n):
"""Advance the iterator n-steps ahead. If n is none, consume entirely."""
deque(itertools.islice(iterator, n), maxlen=0) | [
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private_to_public | () | Reads a private key and outputs the corresponding public key. | Reads a private key and outputs the corresponding public key. | def private_to_public():
"""Reads a private key and outputs the corresponding public key."""
# Parse the CLI options
parser = OptionParser(usage='usage: %prog [options]',
description='Reads a private key and outputs the '
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main | () | Entry point for the GUI-version of Blockify. | Entry point for the GUI-version of Blockify. | def main():
"Entry point for the GUI-version of Blockify."
# Edit this for less or more logging. Loglevel 0 is least verbose.
blockify.init_logger(logpath=None, loglevel=2, quiet=False)
ui = BlockifyUI()
gtk.main() | [
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431,
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Notepad.create_keybinds | (self) | Register Ctrl+Q/W to quit and Ctrl+S to save the blocklist. | Register Ctrl+Q/W to quit and Ctrl+S to save the blocklist. | def create_keybinds(self):
"Register Ctrl+Q/W to quit and Ctrl+S to save the blocklist."
quit_group = gtk.AccelGroup()
quit_group.connect_group(ord("q"), gtk.gdk.CONTROL_MASK,
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quit_group.connect_group(ord("w"), gtk.gdk.CONT... | [
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Notepad.destroy | (self, *args) | Overloading destroy to untoggle the Open List button. | Overloading destroy to untoggle the Open List button. | def destroy(self, *args):
"Overloading destroy to untoggle the Open List button."
super(Notepad, self).destroy()
self.parentw.togglelist.set_active(False) | [
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BlockifyUI.update | (self) | Main GUI loop, 250ms interval (self.update_interval). | Main GUI loop, 250ms interval (self.update_interval). | def update(self):
"Main GUI loop, 250ms interval (self.update_interval)."
# Call the main update function of blockify and assign return value
# (True/False) depending on whether a song to be blocked was found.
self.found = self.b.update()
# Correct the automute state, if necessa... | [
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