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train | is_bool_dtype | Check whether the provided array or dtype is of a boolean dtype.
Parameters
----------
arr_or_dtype : array-like
The array or dtype to check.
Returns
-------
boolean
Whether or not the array or dtype is of a boolean dtype.
Notes
-----
An ExtensionArray is considere... | pandas/core/dtypes/common.py | def is_bool_dtype(arr_or_dtype):
"""
Check whether the provided array or dtype is of a boolean dtype.
Parameters
----------
arr_or_dtype : array-like
The array or dtype to check.
Returns
-------
boolean
Whether or not the array or dtype is of a boolean dtype.
Notes... | def is_bool_dtype(arr_or_dtype):
"""
Check whether the provided array or dtype is of a boolean dtype.
Parameters
----------
arr_or_dtype : array-like
The array or dtype to check.
Returns
-------
boolean
Whether or not the array or dtype is of a boolean dtype.
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train | is_extension_type | Check whether an array-like is of a pandas extension class instance.
Extension classes include categoricals, pandas sparse objects (i.e.
classes represented within the pandas library and not ones external
to it like scipy sparse matrices), and datetime-like arrays.
Parameters
----------
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Check whether an array-like is of a pandas extension class instance.
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classes represented within the pandas library and not ones external
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"""
Check whether an array-like is of a pandas extension class instance.
Extension classes include categoricals, pandas sparse objects (i.e.
classes represented within the pandas library and not ones external
to it like scipy sparse matrices), and datetime-like arrays.
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train | is_extension_array_dtype | Check if an object is a pandas extension array type.
See the :ref:`Use Guide <extending.extension-types>` for more.
Parameters
----------
arr_or_dtype : object
For array-like input, the ``.dtype`` attribute will
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Returns
-------
bool
Whether the `arr_o... | pandas/core/dtypes/common.py | def is_extension_array_dtype(arr_or_dtype):
"""
Check if an object is a pandas extension array type.
See the :ref:`Use Guide <extending.extension-types>` for more.
Parameters
----------
arr_or_dtype : object
For array-like input, the ``.dtype`` attribute will
be extracted.
... | def is_extension_array_dtype(arr_or_dtype):
"""
Check if an object is a pandas extension array type.
See the :ref:`Use Guide <extending.extension-types>` for more.
Parameters
----------
arr_or_dtype : object
For array-like input, the ``.dtype`` attribute will
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train | _is_dtype | Return a boolean if the condition is satisfied for the arr_or_dtype.
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arr_or_dtype : array-like, str, np.dtype, or ExtensionArrayType
The array-like or dtype object whose dtype we want to extract.
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Parameters
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arr_or_dtype : array-like, str, np.dtype, or ExtensionArrayType
The array-like or dtype object whose dtype we want to extract.
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train | _get_dtype | Get the dtype instance associated with an array
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Parameters
----------
arr_or_dtype : array-like
The array-like or dtype object whose dtype we want to extract.
Returns
-------
obj_dtype : The extract dtype instance from the
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"""
Get the dtype instance associated with an array
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Parameters
----------
arr_or_dtype : array-like
The array-like or dtype object whose dtype we want to extract.
Returns
-------
obj_dtype : The extract dtype instance from the
... | def _get_dtype(arr_or_dtype):
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Get the dtype instance associated with an array
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Parameters
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arr_or_dtype : array-like
The array-like or dtype object whose dtype we want to extract.
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train | _is_dtype_type | Return a boolean if the condition is satisfied for the arr_or_dtype.
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arr_or_dtype : array-like
The array-like or dtype object whose dtype we want to extract.
condition : callable[Union[np.dtype, ExtensionDtypeType]]
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arr_or_dtype : array-like
The array-like or dtype object whose dtype we want to extract.
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The array-like or dtype object whose dtype we want to extract.
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train | infer_dtype_from_object | Get a numpy dtype.type-style object for a dtype object.
This methods also includes handling of the datetime64[ns] and
datetime64[ns, TZ] objects.
If no dtype can be found, we return ``object``.
Parameters
----------
dtype : dtype, type
The dtype object whose numpy dtype.type-style
... | pandas/core/dtypes/common.py | def infer_dtype_from_object(dtype):
"""
Get a numpy dtype.type-style object for a dtype object.
This methods also includes handling of the datetime64[ns] and
datetime64[ns, TZ] objects.
If no dtype can be found, we return ``object``.
Parameters
----------
dtype : dtype, type
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"""
Get a numpy dtype.type-style object for a dtype object.
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dtype : dtype, type
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train | _validate_date_like_dtype | Check whether the dtype is a date-like dtype. Raises an error if invalid.
Parameters
----------
dtype : dtype, type
The dtype to check.
Raises
------
TypeError : The dtype could not be casted to a date-like dtype.
ValueError : The dtype is an illegal date-like dtype (e.g. the
... | pandas/core/dtypes/common.py | def _validate_date_like_dtype(dtype):
"""
Check whether the dtype is a date-like dtype. Raises an error if invalid.
Parameters
----------
dtype : dtype, type
The dtype to check.
Raises
------
TypeError : The dtype could not be casted to a date-like dtype.
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Check whether the dtype is a date-like dtype. Raises an error if invalid.
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dtype : dtype, type
The dtype to check.
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train | pandas_dtype | Convert input into a pandas only dtype object or a numpy dtype object.
Parameters
----------
dtype : object to be converted
Returns
-------
np.dtype or a pandas dtype
Raises
------
TypeError if not a dtype | pandas/core/dtypes/common.py | def pandas_dtype(dtype):
"""
Convert input into a pandas only dtype object or a numpy dtype object.
Parameters
----------
dtype : object to be converted
Returns
-------
np.dtype or a pandas dtype
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------
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"""
# short-circuit
if isi... | def pandas_dtype(dtype):
"""
Convert input into a pandas only dtype object or a numpy dtype object.
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dtype : object to be converted
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np.dtype or a pandas dtype
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train | _groupby_and_merge | groupby & merge; we are always performing a left-by type operation
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by: field to group
on: duplicates field
left: left frame
right: right frame
_merge_pieces: function for merging
check_duplicates: boolean, default True
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"""
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Parameters
----------
by: field to group
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left: left frame
right: right frame
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train | merge_asof | Perform an asof merge. This is similar to a left-join except that we
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Both DataFrames must be sorted by the key.
For each row in the left DataFrame:
- A "backward" search selects the last row in the right DataFrame whose
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train | _restore_dropped_levels_multijoin | *this is an internal non-public method*
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Depending on the type of join, this method restores the appropriate
dropped levels of the joined multi-index.
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"""
*this is an internal non-public method*
Returns the levels, labels and names of a multi-index to multi-index join.
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train | _MergeOperation._maybe_restore_index_levels | Restore index levels specified as `on` parameters
Here we check for cases where `self.left_on` and `self.right_on` pairs
each reference an index level in their respective DataFrames. The
joined columns corresponding to these pairs are then restored to the
index of `result`.
**N... | pandas/core/reshape/merge.py | def _maybe_restore_index_levels(self, result):
"""
Restore index levels specified as `on` parameters
Here we check for cases where `self.left_on` and `self.right_on` pairs
each reference an index level in their respective DataFrames. The
joined columns corresponding to these pai... | def _maybe_restore_index_levels(self, result):
"""
Restore index levels specified as `on` parameters
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train | _MergeOperation._get_join_indexers | return the join indexers | pandas/core/reshape/merge.py | def _get_join_indexers(self):
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train | _MergeOperation._create_join_index | Create a join index by rearranging one index to match another
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"""
Create a join index by rearranging one index to match another
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----------
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train | _MergeOperation._get_merge_keys | Note: has side effects (copy/delete key columns)
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train | _AsOfMerge._get_join_indexers | return the join indexers | pandas/core/reshape/merge.py | def _get_join_indexers(self):
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is_dtype : bool
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Parameters
----------
dtype : object
The object to check.
Returns
-------
is_dtype : bool
Notes
-----
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----------
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List of arrays to be concatenated with sep;
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----------
pat : str
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Count occurrences of pattern in each string of the Series/Index.
This function is used to count the number of times a particular regex
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Parameters
----------
pat : st... | def str_count(arr, pat, flags=0):
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Count occurrences of pattern in each string of the Series/Index.
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train | str_contains | Test if pattern or regex is contained within a string of a Series or Index.
Return boolean Series or Index based on whether a given pattern or regex is
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Parameters
----------
pat : str
Character sequence or regular expression.
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Test if pattern or regex is contained within a string of a Series or Index.
Return boolean Series or Index based on whether a given pattern or regex is
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Test if pattern or regex is contained within a string of a Series or Index.
Return boolean Series or Index based on whether a given pattern or regex is
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train | str_startswith | Test if the start of each string element matches a pattern.
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pat : str
Character sequence. Regular expressions are not accepted.
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Object shown if element tested is not a string.
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Test if the start of each string element matches a pattern.
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----------
pat : str
Character sequence. Regular expressions are not accepted.
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Character sequence. Regular expressions are not accepted.
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Test if the end of each string element matches a pattern.
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----------
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Character sequence. Regular expressions are not accepted.
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Test if the end of each string element matches a pattern.
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Parameters
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pat : str or compiled regex
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Same value for all (int) or different value per (sequence).
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----------
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-------
Series or Index of object
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Determine if each string matches a regular expression.
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Character sequence or regular expression.
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Character sequence or regular expression.
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train | _groups_or_na_fun | Used in both extract_noexpand and extract_frame | pandas/core/strings.py | def _groups_or_na_fun(regex):
"""Used in both extract_noexpand and extract_frame"""
if regex.groups == 0:
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m = regex.search... | def _groups_or_na_fun(regex):
"""Used in both extract_noexpand and extract_frame"""
if regex.groups == 0:
raise ValueError("pattern contains no capture groups")
empty_row = [np.nan] * regex.groups
def f(x):
if not isinstance(x, str):
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train | _str_extract_noexpand | Find groups in each string in the Series using passed regular
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str_extract(expand=False), and can return Series, DataFrame, or
Index. | pandas/core/strings.py | def _str_extract_noexpand(arr, pat, flags=0):
"""
Find groups in each string in the Series using passed regular
expression. This function is called from
str_extract(expand=False), and can return Series, DataFrame, or
Index.
"""
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"""
Find groups in each string in the Series using passed regular
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Index.
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train | _str_extract_frame | For each subject string in the Series, extract groups from the
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str_extract(expand=True), and always returns a DataFrame. | pandas/core/strings.py | def _str_extract_frame(arr, pat, flags=0):
"""
For each subject string in the Series, extract groups from the
first match of regular expression pat. This function is called from
str_extract(expand=True), and always returns a DataFrame.
"""
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train | str_extract | r"""
Extract capture groups in the regex `pat` as columns in a DataFrame.
For each subject string in the Series, extract groups from the
first match of regular expression `pat`.
Parameters
----------
pat : str
Regular expression pattern with capturing groups.
flags : int, default 0... | pandas/core/strings.py | def str_extract(arr, pat, flags=0, expand=True):
r"""
Extract capture groups in the regex `pat` as columns in a DataFrame.
For each subject string in the Series, extract groups from the
first match of regular expression `pat`.
Parameters
----------
pat : str
Regular expression patt... | def str_extract(arr, pat, flags=0, expand=True):
r"""
Extract capture groups in the regex `pat` as columns in a DataFrame.
For each subject string in the Series, extract groups from the
first match of regular expression `pat`.
Parameters
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pat : str
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train | str_extractall | r"""
For each subject string in the Series, extract groups from all
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Series has exactly one match, extractall(pat).xs(0, level='match')
is the same as extract(pat).
.. versionadded:: 0.18.0
Parameters
----------
pat : st... | pandas/core/strings.py | def str_extractall(arr, pat, flags=0):
r"""
For each subject string in the Series, extract groups from all
matches of regular expression pat. When each subject string in the
Series has exactly one match, extractall(pat).xs(0, level='match')
is the same as extract(pat).
.. versionadded:: 0.18.0
... | def str_extractall(arr, pat, flags=0):
r"""
For each subject string in the Series, extract groups from all
matches of regular expression pat. When each subject string in the
Series has exactly one match, extractall(pat).xs(0, level='match')
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train | str_get_dummies | Split each string in the Series by sep and return a DataFrame
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Parameters
----------
sep : str, default "|"
String to split on.
Returns
-------
DataFrame
Dummy variables corresponding to values of the Series.
See Also
--------
get_d... | pandas/core/strings.py | def str_get_dummies(arr, sep='|'):
"""
Split each string in the Series by sep and return a DataFrame
of dummy/indicator variables.
Parameters
----------
sep : str, default "|"
String to split on.
Returns
-------
DataFrame
Dummy variables corresponding to values of t... | def str_get_dummies(arr, sep='|'):
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Split each string in the Series by sep and return a DataFrame
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Parameters
----------
sep : str, default "|"
String to split on.
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train | str_findall | Find all occurrences of pattern or regular expression in the Series/Index.
Equivalent to applying :func:`re.findall` to all the elements in the
Series/Index.
Parameters
----------
pat : str
Pattern or regular expression.
flags : int, default 0
Flags from ``re`` module, e.g. `re... | pandas/core/strings.py | def str_findall(arr, pat, flags=0):
"""
Find all occurrences of pattern or regular expression in the Series/Index.
Equivalent to applying :func:`re.findall` to all the elements in the
Series/Index.
Parameters
----------
pat : str
Pattern or regular expression.
flags : int, defa... | def str_findall(arr, pat, flags=0):
"""
Find all occurrences of pattern or regular expression in the Series/Index.
Equivalent to applying :func:`re.findall` to all the elements in the
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Parameters
----------
pat : str
Pattern or regular expression.
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train | str_find | Return indexes in each strings in the Series/Index where the
substring is fully contained between [start:end]. Return -1 on failure.
Parameters
----------
sub : str
Substring being searched.
start : int
Left edge index.
end : int
Right edge index.
side : {'left', 'ri... | pandas/core/strings.py | def str_find(arr, sub, start=0, end=None, side='left'):
"""
Return indexes in each strings in the Series/Index where the
substring is fully contained between [start:end]. Return -1 on failure.
Parameters
----------
sub : str
Substring being searched.
start : int
Left edge in... | def str_find(arr, sub, start=0, end=None, side='left'):
"""
Return indexes in each strings in the Series/Index where the
substring is fully contained between [start:end]. Return -1 on failure.
Parameters
----------
sub : str
Substring being searched.
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train | str_pad | Pad strings in the Series/Index up to width.
Parameters
----------
width : int
Minimum width of resulting string; additional characters will be filled
with character defined in `fillchar`.
side : {'left', 'right', 'both'}, default 'left'
Side from which to fill resulting string.... | pandas/core/strings.py | def str_pad(arr, width, side='left', fillchar=' '):
"""
Pad strings in the Series/Index up to width.
Parameters
----------
width : int
Minimum width of resulting string; additional characters will be filled
with character defined in `fillchar`.
side : {'left', 'right', 'both'}, ... | def str_pad(arr, width, side='left', fillchar=' '):
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Pad strings in the Series/Index up to width.
Parameters
----------
width : int
Minimum width of resulting string; additional characters will be filled
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train | str_slice | Slice substrings from each element in the Series or Index.
Parameters
----------
start : int, optional
Start position for slice operation.
stop : int, optional
Stop position for slice operation.
step : int, optional
Step size for slice operation.
Returns
-------
... | pandas/core/strings.py | def str_slice(arr, start=None, stop=None, step=None):
"""
Slice substrings from each element in the Series or Index.
Parameters
----------
start : int, optional
Start position for slice operation.
stop : int, optional
Stop position for slice operation.
step : int, optional
... | def str_slice(arr, start=None, stop=None, step=None):
"""
Slice substrings from each element in the Series or Index.
Parameters
----------
start : int, optional
Start position for slice operation.
stop : int, optional
Stop position for slice operation.
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train | str_slice_replace | Replace a positional slice of a string with another value.
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----------
start : int, optional
Left index position to use for the slice. If not specified (None),
the slice is unbounded on the left, i.e. slice from the start
of the string.
stop : int, optional
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"""
Replace a positional slice of a string with another value.
Parameters
----------
start : int, optional
Left index position to use for the slice. If not specified (None),
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Replace a positional slice of a string with another value.
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----------
start : int, optional
Left index position to use for the slice. If not specified (None),
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train | str_strip | Strip whitespace (including newlines) from each string in the
Series/Index.
Parameters
----------
to_strip : str or unicode
side : {'left', 'right', 'both'}, default 'both'
Returns
-------
Series or Index | pandas/core/strings.py | def str_strip(arr, to_strip=None, side='both'):
"""
Strip whitespace (including newlines) from each string in the
Series/Index.
Parameters
----------
to_strip : str or unicode
side : {'left', 'right', 'both'}, default 'both'
Returns
-------
Series or Index
"""
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----------
to_strip : str or unicode
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Series or Index
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train | str_wrap | r"""
Wrap long strings in the Series/Index to be formatted in
paragraphs with length less than a given width.
This method has the same keyword parameters and defaults as
:class:`textwrap.TextWrapper`.
Parameters
----------
width : int
Maximum line width.
expand_tabs : bool, opt... | pandas/core/strings.py | def str_wrap(arr, width, **kwargs):
r"""
Wrap long strings in the Series/Index to be formatted in
paragraphs with length less than a given width.
This method has the same keyword parameters and defaults as
:class:`textwrap.TextWrapper`.
Parameters
----------
width : int
Maximum... | def str_wrap(arr, width, **kwargs):
r"""
Wrap long strings in the Series/Index to be formatted in
paragraphs with length less than a given width.
This method has the same keyword parameters and defaults as
:class:`textwrap.TextWrapper`.
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----------
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train | str_get | Extract element from each component at specified position.
Extract element from lists, tuples, or strings in each element in the
Series/Index.
Parameters
----------
i : int
Position of element to extract.
Returns
-------
Series or Index
Examples
--------
>>> s = p... | pandas/core/strings.py | def str_get(arr, i):
"""
Extract element from each component at specified position.
Extract element from lists, tuples, or strings in each element in the
Series/Index.
Parameters
----------
i : int
Position of element to extract.
Returns
-------
Series or Index
Ex... | def str_get(arr, i):
"""
Extract element from each component at specified position.
Extract element from lists, tuples, or strings in each element in the
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Parameters
----------
i : int
Position of element to extract.
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Series or Index
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encoding : str
errors : str, optional
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train | copy | Copy a docstring from another source function (if present) | pandas/core/strings.py | def copy(source):
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train | StringMethods.normalize | Return the Unicode normal form for the strings in the Series/Index.
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form : {'NFC', 'NFKC', 'NFD', 'NFKD'}
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----------
form : {'NFC', 'NFKC', 'NFD', 'NFKD'}
Unicode form
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----------
form : {'NFC', 'NFKC', 'NFD', 'NFKD'}
Unicode form
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train | get_sys_info | Returns system information as a dict | pandas/util/_print_versions.py | def get_sys_info():
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commit = None
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train | whitelist_method_generator | Yields all GroupBy member defs for DataFrame/Series names in whitelist.
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base : class
base class
klass : class
class where members are defined.
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base : class
base class
klass : class
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train | GroupByMixin._dispatch | Dispatch to apply. | pandas/core/groupby/base.py | def _dispatch(name, *args, **kwargs):
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----------
key : string / list of selections
ndim : 1,2
requested ndim of result
subset : object, default None
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ndim : 1,2
requested ndim of result
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key : string / list of selections
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requested ndim of result
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train | to_str | Convert bytes and non-string into Python 3 str | pandas/compat/__init__.py | def to_str(s):
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Convert bytes and non-string into Python 3 str
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if isinstance(s, bytes):
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train | set_function_name | Bind the name/qualname attributes of the function | pandas/compat/__init__.py | def set_function_name(f, name, cls):
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train | raise_with_traceback | Raise exception with existing traceback.
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Raise exception with existing traceback.
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Raise exception with existing traceback.
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train | _OpenpyxlWriter._convert_to_style | converts a style_dict to an openpyxl style object
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----------
style_dict : style dictionary to convert | pandas/io/excel/_openpyxl.py | def _convert_to_style(cls, style_dict):
"""
converts a style_dict to an openpyxl style object
Parameters
----------
style_dict : style dictionary to convert
"""
from openpyxl.style import Style
xls_style = Style()
for key, value in style_dict.item... | def _convert_to_style(cls, style_dict):
"""
converts a style_dict to an openpyxl style object
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style_dict : style dictionary to convert
"""
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train | _OpenpyxlWriter._convert_to_style_kwargs | Convert a style_dict to a set of kwargs suitable for initializing
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style_dict : dict
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style_dict : dict
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train | _OpenpyxlWriter._convert_to_color | Convert ``color_spec`` to an openpyxl v2 Color object
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Convert ``color_spec`` to an openpyxl v2 Color object
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color_spec : str, dict
A 32-bit ARGB hex string, or a dict with zero or more of the
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'rgb'
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Convert ``color_spec`` to an openpyxl v2 Color object
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train | _OpenpyxlWriter._convert_to_font | Convert ``font_dict`` to an openpyxl v2 Font object
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font_dict : dict
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Convert ``fill_dict`` to an openpyxl v2 Fill object
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fill_dict : dict
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Convert ``fill_dict`` to an openpyxl v2 Fill object
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train | _OpenpyxlWriter._convert_to_side | Convert ``side_spec`` to an openpyxl v2 Side object
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Convert ``side_spec`` to an openpyxl v2 Side object
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side_spec : str, dict
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Convert ``side_spec`` to an openpyxl v2 Side object
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train | _OpenpyxlWriter._convert_to_border | Convert ``border_dict`` to an openpyxl v2 Border object
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border_dict : dict
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train | frame_apply | construct and return a row or column based frame apply object | pandas/core/apply.py | def frame_apply(obj, func, axis=0, broadcast=None,
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train | FrameApply.get_result | compute the results | pandas/core/apply.py | def get_result(self):
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train | FrameApply.apply_raw | apply to the values as a numpy array | pandas/core/apply.py | def apply_raw(self):
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train | FrameRowApply.wrap_results_for_axis | return the results for the rows | pandas/core/apply.py | def wrap_results_for_axis(self):
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train | FrameColumnApply.wrap_results_for_axis | return the results for the columns | pandas/core/apply.py | def wrap_results_for_axis(self):
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train | FrameColumnApply.infer_to_same_shape | infer the results to the same shape as the input object | pandas/core/apply.py | def infer_to_same_shape(self):
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train | cartesian_product | Numpy version of itertools.product.
Sometimes faster (for large inputs)...
Parameters
----------
X : list-like of list-likes
Returns
-------
product : list of ndarrays
Examples
--------
>>> cartesian_product([list('ABC'), [1, 2]])
[array(['A', 'A', 'B', 'B', 'C', 'C'], dty... | pandas/core/reshape/util.py | def cartesian_product(X):
"""
Numpy version of itertools.product.
Sometimes faster (for large inputs)...
Parameters
----------
X : list-like of list-likes
Returns
-------
product : list of ndarrays
Examples
--------
>>> cartesian_product([list('ABC'), [1, 2]])
[arr... | def cartesian_product(X):
"""
Numpy version of itertools.product.
Sometimes faster (for large inputs)...
Parameters
----------
X : list-like of list-likes
Returns
-------
product : list of ndarrays
Examples
--------
>>> cartesian_product([list('ABC'), [1, 2]])
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train | _strip_schema | Returns the url without the s3:// part | pandas/io/s3.py | def _strip_schema(url):
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train | xception | Preview version of Xception network. Not tested yet - use at own risk. No pretrained model yet. | fastai/vision/models/xception.py | def xception(c, k=8, n_middle=8):
"Preview version of Xception network. Not tested yet - use at own risk. No pretrained model yet."
layers = [
conv(3, k*4, 3, 2),
conv(k*4, k*8, 3),
ConvSkip(k*8, k*16, act=False),
ConvSkip(k*16, k*32),
ConvSkip(k*32, k*91),
]
for ... | def xception(c, k=8, n_middle=8):
"Preview version of Xception network. Not tested yet - use at own risk. No pretrained model yet."
layers = [
conv(3, k*4, 3, 2),
conv(k*4, k*8, 3),
ConvSkip(k*8, k*16, act=False),
ConvSkip(k*16, k*32),
ConvSkip(k*32, k*91),
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train | LanguageModelData.get_model | Method returns a RNN_Learner object, that wraps an instance of the RNN_Encoder module.
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emb_sz (int): embedding size
n_hid (int): number of hidden inputs
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Args:
opt_fn (Optimizer): the torch optimizer function to use
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train | LanguageModelData.from_text_files | Method used to instantiate a LanguageModelData object that can be used for a
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path (str): the absolute path in which temporary model data will be saved
field (Field): torchtext field
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supported nlp task.
Args:
path (str): the absolute path in which temporary model data will be saved
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train | get_files | Return list of files in `path` that have a suffix in `extensions`; optionally `recurse`. | fastai/data_block.py | def get_files(path:PathOrStr, extensions:Collection[str]=None, recurse:bool=False,
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train | _databunch_load_empty | Load an empty `DataBunch` from the exported file in `path/fname` with optional `tfms`. | fastai/data_block.py | def _databunch_load_empty(cls, path, fname:str='export.pkl'):
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train | ItemList.process | Apply `processor` or `self.processor` to `self`. | fastai/data_block.py | def process(self, processor:PreProcessors=None):
"Apply `processor` or `self.processor` to `self`."
if processor is not None: self.processor = processor
self.processor = listify(self.processor)
for p in self.processor: p.process(self)
return self | def process(self, processor:PreProcessors=None):
"Apply `processor` or `self.processor` to `self`."
if processor is not None: self.processor = processor
self.processor = listify(self.processor)
for p in self.processor: p.process(self)
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train | ItemList.process_one | Apply `processor` or `self.processor` to `item`. | fastai/data_block.py | def process_one(self, item:ItemBase, processor:PreProcessors=None):
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train | ItemList.reconstruct | Reconstruct one of the underlying item for its data `t`. | fastai/data_block.py | def reconstruct(self, t:Tensor, x:Tensor=None):
"Reconstruct one of the underlying item for its data `t`."
return self[0].reconstruct(t,x) if has_arg(self[0].reconstruct, 'x') else self[0].reconstruct(t) | def reconstruct(self, t:Tensor, x:Tensor=None):
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train | ItemList.new | Create a new `ItemList` from `items`, keeping the same attributes. | fastai/data_block.py | def new(self, items:Iterator, processor:PreProcessors=None, **kwargs)->'ItemList':
"Create a new `ItemList` from `items`, keeping the same attributes."
processor = ifnone(processor, self.processor)
copy_d = {o:getattr(self,o) for o in self.copy_new}
kwargs = {**copy_d, **kwargs}
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processor = ifnone(processor, self.processor)
copy_d = {o:getattr(self,o) for o in self.copy_new}
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train | ItemList.from_folder | Create an `ItemList` in `path` from the filenames that have a suffix in `extensions`.
`recurse` determines if we search subfolders. | fastai/data_block.py | def from_folder(cls, path:PathOrStr, extensions:Collection[str]=None, recurse:bool=True,
include:Optional[Collection[str]]=None, processor:PreProcessors=None, **kwargs)->'ItemList':
"""Create an `ItemList` in `path` from the filenames that have a suffix in `extensions`.
`recurse` det... | def from_folder(cls, path:PathOrStr, extensions:Collection[str]=None, recurse:bool=True,
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train | ItemList.from_df | Create an `ItemList` in `path` from the inputs in the `cols` of `df`. | fastai/data_block.py | def from_df(cls, df:DataFrame, path:PathOrStr='.', cols:IntsOrStrs=0, processor:PreProcessors=None, **kwargs)->'ItemList':
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train | ItemList.from_csv | Create an `ItemList` in `path` from the inputs in the `cols` of `path/csv_name` | fastai/data_block.py | def from_csv(cls, path:PathOrStr, csv_name:str, cols:IntsOrStrs=0, delimiter:str=None, header:str='infer',
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train | ItemList.use_partial_data | Use only a sample of `sample_pct`of the full dataset and an optional `seed`. | fastai/data_block.py | def use_partial_data(self, sample_pct:float=0.01, seed:int=None)->'ItemList':
"Use only a sample of `sample_pct`of the full dataset and an optional `seed`."
if seed is not None: np.random.seed(seed)
rand_idx = np.random.permutation(range_of(self))
cut = int(sample_pct * len(self))
... | def use_partial_data(self, sample_pct:float=0.01, seed:int=None)->'ItemList':
"Use only a sample of `sample_pct`of the full dataset and an optional `seed`."
if seed is not None: np.random.seed(seed)
rand_idx = np.random.permutation(range_of(self))
cut = int(sample_pct * len(self))
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train | ItemList.to_text | Save `self.items` to `fn` in `self.path`. | fastai/data_block.py | def to_text(self, fn:str):
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with open(self.path/fn, 'w') as f: f.writelines([f'{o}\n' for o in self._relative_item_paths()]) | def to_text(self, fn:str):
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self.items = array([o for o in self.items if func(o)])
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train | ItemList.filter_by_folder | Only keep filenames in `include` folder or reject the ones in `exclude`. | fastai/data_block.py | def filter_by_folder(self, include=None, exclude=None):
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include,exclude = listify(include),listify(exclude)
def _inner(o):
if isinstance(o, Path): n = o.relative_to(self.path).parts[0]
else: n = o.... | def filter_by_folder(self, include=None, exclude=None):
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train | ItemList.filter_by_rand | Keep random sample of `items` with probability `p` and an optional `seed`. | fastai/data_block.py | def filter_by_rand(self, p:float, seed:int=None):
"Keep random sample of `items` with probability `p` and an optional `seed`."
if seed is not None: np.random.seed(seed)
return self.filter_by_func(lambda o: rand_bool(p)) | def filter_by_rand(self, p:float, seed:int=None):
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if seed is not None: np.random.seed(seed)
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train | ItemList.split_none | Don't split the data and create an empty validation set. | fastai/data_block.py | def split_none(self):
"Don't split the data and create an empty validation set."
val = self[[]]
val.ignore_empty = True
return self._split(self.path, self, val) | def split_none(self):
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train | ItemList.split_by_list | Split the data between `train` and `valid`. | fastai/data_block.py | def split_by_list(self, train, valid):
"Split the data between `train` and `valid`."
return self._split(self.path, train, valid) | def split_by_list(self, train, valid):
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train | ItemList.split_by_idxs | Split the data between `train_idx` and `valid_idx`. | fastai/data_block.py | def split_by_idxs(self, train_idx, valid_idx):
"Split the data between `train_idx` and `valid_idx`."
return self.split_by_list(self[train_idx], self[valid_idx]) | def split_by_idxs(self, train_idx, valid_idx):
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train | ItemList.split_by_idx | Split the data according to the indexes in `valid_idx`. | fastai/data_block.py | def split_by_idx(self, valid_idx:Collection[int])->'ItemLists':
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#train_idx = [i for i in range_of(self.items) if i not in valid_idx]
train_idx = np.setdiff1d(arange_of(self.items), valid_idx)
return self.split_by_idxs(train_idx, v... | def split_by_idx(self, valid_idx:Collection[int])->'ItemLists':
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train | ItemList.split_by_folder | Split the data depending on the folder (`train` or `valid`) in which the filenames are. | fastai/data_block.py | def split_by_folder(self, train:str='train', valid:str='valid')->'ItemLists':
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return self.split_by_idxs(self._get_by_folder(train), self._get_by_folder(valid)) | def split_by_folder(self, train:str='train', valid:str='valid')->'ItemLists':
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train | ItemList.split_by_rand_pct | Split the items randomly by putting `valid_pct` in the validation set, optional `seed` can be passed. | fastai/data_block.py | def split_by_rand_pct(self, valid_pct:float=0.2, seed:int=None)->'ItemLists':
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if valid_pct==0.: return self.split_none()
if seed is not None: np.random.seed(seed)
rand_idx = np.random.... | def split_by_rand_pct(self, valid_pct:float=0.2, seed:int=None)->'ItemLists':
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if valid_pct==0.: return self.split_none()
if seed is not None: np.random.seed(seed)
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train | ItemList.split_subsets | Split the items into train set with size `train_size * n` and valid set with size `valid_size * n`. | fastai/data_block.py | def split_subsets(self, train_size:float, valid_size:float, seed=None) -> 'ItemLists':
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assert 0 < train_size < 1
assert 0 < valid_size < 1
assert train_size + valid_size <= 1.
if... | def split_subsets(self, train_size:float, valid_size:float, seed=None) -> 'ItemLists':
"Split the items into train set with size `train_size * n` and valid set with size `valid_size * n`."
assert 0 < train_size < 1
assert 0 < valid_size < 1
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train | ItemList.split_by_valid_func | Split the data by result of `func` (which returns `True` for validation set). | fastai/data_block.py | def split_by_valid_func(self, func:Callable)->'ItemLists':
"Split the data by result of `func` (which returns `True` for validation set)."
valid_idx = [i for i,o in enumerate(self.items) if func(o)]
return self.split_by_idx(valid_idx) | def split_by_valid_func(self, func:Callable)->'ItemLists':
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valid_idx = [i for i,o in enumerate(self.items) if func(o)]
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train | ItemList.split_by_files | Split the data by using the names in `valid_names` for validation. | fastai/data_block.py | def split_by_files(self, valid_names:'ItemList')->'ItemLists':
"Split the data by using the names in `valid_names` for validation."
if isinstance(self.items[0], Path): return self.split_by_valid_func(lambda o: o.name in valid_names)
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if isinstance(self.items[0], Path): return self.split_by_valid_func(lambda o: o.name in valid_names)
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train | ItemList.split_by_fname_file | Split the data by using the names in `fname` for the validation set. `path` will override `self.path`. | fastai/data_block.py | def split_by_fname_file(self, fname:PathOrStr, path:PathOrStr=None)->'ItemLists':
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path = Path(ifnone(path, self.path))
valid_names = loadtxt_str(path/fname)
return self.split_by_files... | def split_by_fname_file(self, fname:PathOrStr, path:PathOrStr=None)->'ItemLists':
"Split the data by using the names in `fname` for the validation set. `path` will override `self.path`."
path = Path(ifnone(path, self.path))
valid_names = loadtxt_str(path/fname)
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train | ItemList.split_from_df | Split the data from the `col` in the dataframe in `self.inner_df`. | fastai/data_block.py | def split_from_df(self, col:IntsOrStrs=2):
"Split the data from the `col` in the dataframe in `self.inner_df`."
valid_idx = np.where(self.inner_df.iloc[:,df_names_to_idx(col, self.inner_df)])[0]
return self.split_by_idx(valid_idx) | def split_from_df(self, col:IntsOrStrs=2):
"Split the data from the `col` in the dataframe in `self.inner_df`."
valid_idx = np.where(self.inner_df.iloc[:,df_names_to_idx(col, self.inner_df)])[0]
return self.split_by_idx(valid_idx) | [
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train | ItemList.get_label_cls | Return `label_cls` or guess one from the first element of `labels`. | fastai/data_block.py | def get_label_cls(self, labels, label_cls:Callable=None, label_delim:str=None, **kwargs):
"Return `label_cls` or guess one from the first element of `labels`."
if label_cls is not None: return label_cls
if self.label_cls is not None: return self.label_cls
if label_... | def get_label_cls(self, labels, label_cls:Callable=None, label_delim:str=None, **kwargs):
"Return `label_cls` or guess one from the first element of `labels`."
if label_cls is not None: return label_cls
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