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train | BasePandasDataset.le | Checks element-wise that this is less than or equal to other.
Args:
other: A DataFrame or Series or scalar to compare to.
axis: The axis to perform the le over.
level: The Multilevel index level to apply le over.
Returns:
A new DataFrame filled w... | modin/pandas/base.py | def le(self, other, axis="columns", level=None):
"""Checks element-wise that this is less than or equal to other.
Args:
other: A DataFrame or Series or scalar to compare to.
axis: The axis to perform the le over.
level: The Multilevel index level to apply le ov... | def le(self, other, axis="columns", level=None):
"""Checks element-wise that this is less than or equal to other.
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other: A DataFrame or Series or scalar to compare to.
axis: The axis to perform the le over.
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train | BasePandasDataset.lt | Checks element-wise that this is less than other.
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other: A DataFrame or Series or scalar to compare to.
axis: The axis to perform the lt over.
level: The Multilevel index level to apply lt over.
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"""Checks element-wise that this is less than other.
Args:
other: A DataFrame or Series or scalar to compare to.
axis: The axis to perform the lt over.
level: The Multilevel index level to apply lt over.
... | def lt(self, other, axis="columns", level=None):
"""Checks element-wise that this is less than other.
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other: A DataFrame or Series or scalar to compare to.
axis: The axis to perform the lt over.
level: The Multilevel index level to apply lt over.
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train | BasePandasDataset.mean | Computes mean across the DataFrame.
Args:
axis (int): The axis to take the mean on.
skipna (bool): True to skip NA values, false otherwise.
Returns:
The mean of the DataFrame. (Pandas series) | modin/pandas/base.py | def mean(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs):
"""Computes mean across the DataFrame.
Args:
axis (int): The axis to take the mean on.
skipna (bool): True to skip NA values, false otherwise.
Returns:
The mean of the D... | def mean(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs):
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axis (int): The axis to take the mean on.
skipna (bool): True to skip NA values, false otherwise.
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train | BasePandasDataset.median | Computes median across the DataFrame.
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axis (int): The axis to take the median on.
skipna (bool): True to skip NA values, false otherwise.
Returns:
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"""Computes median across the DataFrame.
Args:
axis (int): The axis to take the median on.
skipna (bool): True to skip NA values, false otherwise.
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The median ... | def median(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs):
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axis (int): The axis to take the median on.
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train | BasePandasDataset.memory_usage | Returns the memory usage of each column in bytes
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index (bool): Whether to include the memory usage of the DataFrame's
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deep (bool): If True, introspect the data deeply by interrogating
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"""Returns the memory usage of each column in bytes
Args:
index (bool): Whether to include the memory usage of the DataFrame's
index in returned Series. Defaults to True
deep (bool): If True, introspect the da... | def memory_usage(self, index=True, deep=False):
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index (bool): Whether to include the memory usage of the DataFrame's
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train | BasePandasDataset.min | Perform min across the DataFrame.
Args:
axis (int): The axis to take the min on.
skipna (bool): True to skip NA values, false otherwise.
Returns:
The min of the DataFrame. | modin/pandas/base.py | def min(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs):
"""Perform min across the DataFrame.
Args:
axis (int): The axis to take the min on.
skipna (bool): True to skip NA values, false otherwise.
Returns:
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axis (int): The axis to take the min on.
skipna (bool): True to skip NA values, false otherwise.
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train | BasePandasDataset.mod | Mods this DataFrame against another DataFrame/Series/scalar.
Args:
other: The object to use to apply the mod against this.
axis: The axis to mod over.
level: The Multilevel index level to apply mod over.
fill_value: The value to fill NaNs with.
R... | modin/pandas/base.py | def mod(self, other, axis="columns", level=None, fill_value=None):
"""Mods this DataFrame against another DataFrame/Series/scalar.
Args:
other: The object to use to apply the mod against this.
axis: The axis to mod over.
level: The Multilevel index level to app... | def mod(self, other, axis="columns", level=None, fill_value=None):
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train | BasePandasDataset.mode | Perform mode across the DataFrame.
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axis (int): The axis to take the mode on.
numeric_only (bool): if True, only apply to numeric columns.
Returns:
DataFrame: The mode of the DataFrame. | modin/pandas/base.py | def mode(self, axis=0, numeric_only=False, dropna=True):
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Args:
axis (int): The axis to take the mode on.
numeric_only (bool): if True, only apply to numeric columns.
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DataFrame: The mode of the DataFrame.... | def mode(self, axis=0, numeric_only=False, dropna=True):
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axis (int): The axis to take the mode on.
numeric_only (bool): if True, only apply to numeric columns.
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train | BasePandasDataset.mul | Multiplies this DataFrame against another DataFrame/Series/scalar.
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other: The object to use to apply the multiply against this.
axis: The axis to multiply over.
level: The Multilevel index level to apply multiply over.
fill_value: The value to fill Na... | modin/pandas/base.py | def mul(self, other, axis="columns", level=None, fill_value=None):
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other: The object to use to apply the multiply against this.
axis: The axis to multiply over.
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train | BasePandasDataset.ne | Checks element-wise that this is not equal to other.
Args:
other: A DataFrame or Series or scalar to compare to.
axis: The axis to perform the ne over.
level: The Multilevel index level to apply ne over.
Returns:
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"""Checks element-wise that this is not equal to other.
Args:
other: A DataFrame or Series or scalar to compare to.
axis: The axis to perform the ne over.
level: The Multilevel index level to apply ne over.
... | def ne(self, other, axis="columns", level=None):
"""Checks element-wise that this is not equal to other.
Args:
other: A DataFrame or Series or scalar to compare to.
axis: The axis to perform the ne over.
level: The Multilevel index level to apply ne over.
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train | BasePandasDataset.nunique | Return Series with number of distinct
observations over requested axis.
Args:
axis : {0 or 'index', 1 or 'columns'}, default 0
dropna : boolean, default True
Returns:
nunique : Series | modin/pandas/base.py | def nunique(self, axis=0, dropna=True):
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observations over requested axis.
Args:
axis : {0 or 'index', 1 or 'columns'}, default 0
dropna : boolean, default True
Returns:
nunique : Series
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axis : {0 or 'index', 1 or 'columns'}, default 0
dropna : boolean, default True
Returns:
nunique : Series
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train | BasePandasDataset.pow | Pow this DataFrame against another DataFrame/Series/scalar.
Args:
other: The object to use to apply the pow against this.
axis: The axis to pow over.
level: The Multilevel index level to apply pow over.
fill_value: The value to fill NaNs with.
Re... | modin/pandas/base.py | def pow(self, other, axis="columns", level=None, fill_value=None):
"""Pow this DataFrame against another DataFrame/Series/scalar.
Args:
other: The object to use to apply the pow against this.
axis: The axis to pow over.
level: The Multilevel index level to appl... | def pow(self, other, axis="columns", level=None, fill_value=None):
"""Pow this DataFrame against another DataFrame/Series/scalar.
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other: The object to use to apply the pow against this.
axis: The axis to pow over.
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train | BasePandasDataset.prod | Return the product of the values for the requested axis
Args:
axis : {index (0), columns (1)}
skipna : boolean, default True
level : int or level name, default None
numeric_only : boolean, default None
min_count : int, default 0
Retu... | modin/pandas/base.py | def prod(
self,
axis=None,
skipna=None,
level=None,
numeric_only=None,
min_count=0,
**kwargs
):
"""Return the product of the values for the requested axis
Args:
axis : {index (0), columns (1)}
skipna : bool... | def prod(
self,
axis=None,
skipna=None,
level=None,
numeric_only=None,
min_count=0,
**kwargs
):
"""Return the product of the values for the requested axis
Args:
axis : {index (0), columns (1)}
skipna : bool... | [
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train | BasePandasDataset.quantile | Return values at the given quantile over requested axis,
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Args:
q (float): 0 <= q <= 1, the quantile(s) to compute
axis (int): 0 or 'index' for row-wise,
1 or 'columns' for column-wise
interpolation: {'linear',... | modin/pandas/base.py | def quantile(self, q=0.5, axis=0, numeric_only=True, interpolation="linear"):
"""Return values at the given quantile over requested axis,
a la numpy.percentile.
Args:
q (float): 0 <= q <= 1, the quantile(s) to compute
axis (int): 0 or 'index' for row-wise,
... | def quantile(self, q=0.5, axis=0, numeric_only=True, interpolation="linear"):
"""Return values at the given quantile over requested axis,
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Args:
q (float): 0 <= q <= 1, the quantile(s) to compute
axis (int): 0 or 'index' for row-wise,
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train | BasePandasDataset.rank | Compute numerical data ranks (1 through n) along axis.
Equal values are assigned a rank that is the [method] of
the ranks of those values.
Args:
axis (int): 0 or 'index' for row-wise,
1 or 'columns' for column-wise
method: {'average', 'min'... | modin/pandas/base.py | def rank(
self,
axis=0,
method="average",
numeric_only=None,
na_option="keep",
ascending=True,
pct=False,
):
"""
Compute numerical data ranks (1 through n) along axis.
Equal values are assigned a rank that is the [method] of
... | def rank(
self,
axis=0,
method="average",
numeric_only=None,
na_option="keep",
ascending=True,
pct=False,
):
"""
Compute numerical data ranks (1 through n) along axis.
Equal values are assigned a rank that is the [method] of
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train | BasePandasDataset.reset_index | Reset this index to default and create column from current index.
Args:
level: Only remove the given levels from the index. Removes all
levels by default
drop: Do not try to insert index into DataFrame columns. This
resets the index to the default i... | modin/pandas/base.py | def reset_index(
self, level=None, drop=False, inplace=False, col_level=0, col_fill=""
):
"""Reset this index to default and create column from current index.
Args:
level: Only remove the given levels from the index. Removes all
levels by default
... | def reset_index(
self, level=None, drop=False, inplace=False, col_level=0, col_fill=""
):
"""Reset this index to default and create column from current index.
Args:
level: Only remove the given levels from the index. Removes all
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train | BasePandasDataset.rmod | Mod this DataFrame against another DataFrame/Series/scalar.
Args:
other: The object to use to apply the div against this.
axis: The axis to div over.
level: The Multilevel index level to apply div over.
fill_value: The value to fill NaNs with.
Re... | modin/pandas/base.py | def rmod(self, other, axis="columns", level=None, fill_value=None):
"""Mod this DataFrame against another DataFrame/Series/scalar.
Args:
other: The object to use to apply the div against this.
axis: The axis to div over.
level: The Multilevel index level to app... | def rmod(self, other, axis="columns", level=None, fill_value=None):
"""Mod this DataFrame against another DataFrame/Series/scalar.
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train | BasePandasDataset.round | Round each element in the DataFrame.
Args:
decimals: The number of decimals to round to.
Returns:
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"""Round each element in the DataFrame.
Args:
decimals: The number of decimals to round to.
Returns:
A new DataFrame.
"""
return self.__constructor__(
query_compiler=self._query_compile... | def round(self, decimals=0, *args, **kwargs):
"""Round each element in the DataFrame.
Args:
decimals: The number of decimals to round to.
Returns:
A new DataFrame.
"""
return self.__constructor__(
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train | BasePandasDataset.rpow | Pow this DataFrame against another DataFrame/Series/scalar.
Args:
other: The object to use to apply the pow against this.
axis: The axis to pow over.
level: The Multilevel index level to apply pow over.
fill_value: The value to fill NaNs with.
Re... | modin/pandas/base.py | def rpow(self, other, axis="columns", level=None, fill_value=None):
"""Pow this DataFrame against another DataFrame/Series/scalar.
Args:
other: The object to use to apply the pow against this.
axis: The axis to pow over.
level: The Multilevel index level to app... | def rpow(self, other, axis="columns", level=None, fill_value=None):
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Args:
other: The object to use to apply the pow against this.
axis: The axis to pow over.
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train | BasePandasDataset.rsub | Subtract a DataFrame/Series/scalar from this DataFrame.
Args:
other: The object to use to apply the subtraction to this.
axis: The axis to apply the subtraction over.
level: Mutlilevel index level to subtract over.
fill_value: The value to fill NaNs with.
... | modin/pandas/base.py | def rsub(self, other, axis="columns", level=None, fill_value=None):
"""Subtract a DataFrame/Series/scalar from this DataFrame.
Args:
other: The object to use to apply the subtraction to this.
axis: The axis to apply the subtraction over.
level: Mutlilevel index... | def rsub(self, other, axis="columns", level=None, fill_value=None):
"""Subtract a DataFrame/Series/scalar from this DataFrame.
Args:
other: The object to use to apply the subtraction to this.
axis: The axis to apply the subtraction over.
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train | BasePandasDataset.rtruediv | Div this DataFrame against another DataFrame/Series/scalar.
Args:
other: The object to use to apply the div against this.
axis: The axis to div over.
level: The Multilevel index level to apply div over.
fill_value: The value to fill NaNs with.
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"""Div this DataFrame against another DataFrame/Series/scalar.
Args:
other: The object to use to apply the div against this.
axis: The axis to div over.
level: The Multilevel index level to... | def rtruediv(self, other, axis="columns", level=None, fill_value=None):
"""Div this DataFrame against another DataFrame/Series/scalar.
Args:
other: The object to use to apply the div against this.
axis: The axis to div over.
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train | BasePandasDataset.sample | Returns a random sample of items from an axis of object.
Args:
n: Number of items from axis to return. Cannot be used with frac.
Default = 1 if frac = None.
frac: Fraction of axis items to return. Cannot be used with n.
replace: Sample with or without r... | modin/pandas/base.py | def sample(
self,
n=None,
frac=None,
replace=False,
weights=None,
random_state=None,
axis=None,
):
"""Returns a random sample of items from an axis of object.
Args:
n: Number of items from axis to return. Cannot be used... | def sample(
self,
n=None,
frac=None,
replace=False,
weights=None,
random_state=None,
axis=None,
):
"""Returns a random sample of items from an axis of object.
Args:
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train | BasePandasDataset.set_axis | Assign desired index to given axis.
Args:
labels (pandas.Index or list-like): The Index to assign.
axis (string or int): The axis to reassign.
inplace (bool): Whether to make these modifications inplace.
Returns:
If inplace is False, returns a ne... | modin/pandas/base.py | def set_axis(self, labels, axis=0, inplace=None):
"""Assign desired index to given axis.
Args:
labels (pandas.Index or list-like): The Index to assign.
axis (string or int): The axis to reassign.
inplace (bool): Whether to make these modifications inplace.
... | def set_axis(self, labels, axis=0, inplace=None):
"""Assign desired index to given axis.
Args:
labels (pandas.Index or list-like): The Index to assign.
axis (string or int): The axis to reassign.
inplace (bool): Whether to make these modifications inplace.
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train | BasePandasDataset.sort_index | Sort a DataFrame by one of the indices (columns or index).
Args:
axis: The axis to sort over.
level: The MultiIndex level to sort over.
ascending: Ascending or descending
inplace: Whether or not to update this DataFrame inplace.
kind: How to pe... | modin/pandas/base.py | def sort_index(
self,
axis=0,
level=None,
ascending=True,
inplace=False,
kind="quicksort",
na_position="last",
sort_remaining=True,
by=None,
):
"""Sort a DataFrame by one of the indices (columns or index).
Args:
... | def sort_index(
self,
axis=0,
level=None,
ascending=True,
inplace=False,
kind="quicksort",
na_position="last",
sort_remaining=True,
by=None,
):
"""Sort a DataFrame by one of the indices (columns or index).
Args:
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train | BasePandasDataset.sort_values | Sorts by a column/row or list of columns/rows.
Args:
by: A list of labels for the axis to sort over.
axis: The axis to sort.
ascending: Sort in ascending or descending order.
inplace: If true, do the operation inplace.
kind: How to sort.
... | modin/pandas/base.py | def sort_values(
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ascending=True,
inplace=False,
kind="quicksort",
na_position="last",
):
"""Sorts by a column/row or list of columns/rows.
Args:
by: A list of labels for the axis to sort over.
... | def sort_values(
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inplace=False,
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na_position="last",
):
"""Sorts by a column/row or list of columns/rows.
Args:
by: A list of labels for the axis to sort over.
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train | BasePandasDataset.sub | Subtract a DataFrame/Series/scalar from this DataFrame.
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axis: The axis to apply the subtraction over.
level: Mutlilevel index level to subtract over.
fill_value: The value to fill NaNs with.
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axis: The axis to apply the subtraction over.
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train | BasePandasDataset.to_numpy | Convert the DataFrame to a NumPy array.
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train | BasePandasDataset.truediv | Divides this DataFrame against another DataFrame/Series/scalar.
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axis: The axis to divide over.
level: The Multilevel index level to apply divide over.
fill_value: The value to fill NaNs with.
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"""Divides this DataFrame against another DataFrame/Series/scalar.
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other: The object to use to apply the divide against this.
axis: The axis to divide over.
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train | BasePandasDataset.var | Computes variance across the DataFrame.
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skipna (bool): True to skip NA values, false otherwise.
ddof (int): degrees of freedom
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"""Computes variance across the DataFrame.
Args:
axis (int): The axis to take the variance on.
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ddof (... | def var(
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train | BasePandasDataset.size | Get the number of elements in the DataFrame.
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"""Get the number of elements in the DataFrame.
Returns:
The number of elements in the DataFrame.
"""
return len(self._query_compiler.index) * len(self._query_compiler.columns) | def size(self):
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train | PandasOnPythonFramePartition.get | Flushes the call_queue and returns the data.
Note: Since this object is a simple wrapper, just return the data.
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Note: Since this object is a simple wrapper, just return the data.
Returns:
The object that was `put`.
"""
if self.call_queue:
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train | PandasOnPythonFramePartition.apply | Apply some callable function to the data in this partition.
Note: It is up to the implementation how kwargs are handled. They are
an important part of many implementations. As of right now, they
are not serialized.
Args:
func: The lambda to apply (may already be cor... | modin/engines/python/pandas_on_python/frame/partition.py | def apply(self, func, **kwargs):
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an important part of many implementations. As of right now, they
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train | DaskFramePartition.apply | Apply some callable function to the data in this partition.
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train | DaskFramePartition.add_to_apply_calls | Add the function to the apply function call stack.
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train | _read_csv_with_offset_pyarrow_on_ray | Use a Ray task to read a chunk of a CSV into a pyarrow Table.
Note: Ray functions are not detected by codecov (thus pragma: no cover)
Args:
fname: The filename of the file to open.
num_splits: The number of splits (partitions) to separate the DataFrame into.
start: The start byte offse... | modin/experimental/engines/pyarrow_on_ray/io.py | def _read_csv_with_offset_pyarrow_on_ray(
fname, num_splits, start, end, kwargs, header
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"""Use a Ray task to read a chunk of a CSV into a pyarrow Table.
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fname: The filename of the file to open.... | def _read_csv_with_offset_pyarrow_on_ray(
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train | compute_chunksize | Computes the number of rows and/or columns to include in each partition.
Args:
df: The DataFrame to split.
num_splits: The maximum number of splits to separate the DataFrame into.
default_block_size: Minimum number of rows/columns (default set to 32x32).
axis: The axis to split. (0:... | modin/data_management/utils.py | def compute_chunksize(df, num_splits, default_block_size=32, axis=None):
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Args:
df: The DataFrame to split.
num_splits: The maximum number of splits to separate the DataFrame into.
default_block_size: Minimum num... | def compute_chunksize(df, num_splits, default_block_size=32, axis=None):
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df: The DataFrame to split.
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train | _get_nan_block_id | A memory efficient way to get a block of NaNs.
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partition_class (BaseFramePartition): The class to use to put the object
in the remote format.
n_row(int): The number of rows.
n_col(int): The number of columns.
transpose(bool): If true, swap rows and columns.
Ret... | modin/data_management/utils.py | def _get_nan_block_id(partition_class, n_row=1, n_col=1, transpose=False):
"""A memory efficient way to get a block of NaNs.
Args:
partition_class (BaseFramePartition): The class to use to put the object
in the remote format.
n_row(int): The number of rows.
n_col(int): The n... | def _get_nan_block_id(partition_class, n_row=1, n_col=1, transpose=False):
"""A memory efficient way to get a block of NaNs.
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partition_class (BaseFramePartition): The class to use to put the object
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train | split_result_of_axis_func_pandas | Split the Pandas result evenly based on the provided number of splits.
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Args:
axis: The axis to split across.
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train | _is_enlargement | Determine if a locator will enlarge the global index.
Enlargement happens when you trying to locate using labels isn't in the
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train | _compute_ndim | Compute the ndim of result from locators | modin/pandas/indexing.py | def _compute_ndim(row_loc, col_loc):
"""Compute the ndim of result from locators
"""
row_scaler = is_scalar(row_loc)
col_scaler = is_scalar(col_loc)
if row_scaler and col_scaler:
ndim = 0
elif row_scaler ^ col_scaler:
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return ndim | def _compute_ndim(row_loc, col_loc):
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train | _LocationIndexerBase._broadcast_item | Use numpy to broadcast or reshape item.
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Notes:
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# It is valid to pass a DataFrame or Series to __setitem__ that is larger than
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train | _LocationIndexerBase._write_items | Perform remote write and replace blocks. | modin/pandas/indexing.py | def _write_items(self, row_lookup, col_lookup, item):
"""Perform remote write and replace blocks.
"""
self.qc.write_items(row_lookup, col_lookup, item) | def _write_items(self, row_lookup, col_lookup, item):
"""Perform remote write and replace blocks.
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train | _LocIndexer._handle_enlargement | Handle Enlargement (if there is one).
Returns:
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"""Handle Enlargement (if there is one).
Returns:
None
"""
if _is_enlargement(row_loc, self.qc.index) or _is_enlargement(
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Returns:
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train | _LocIndexer._compute_enlarge_labels | Helper for _enlarge_axis, compute common labels and extra labels.
Returns:
nan_labels: The labels needs to be added | modin/pandas/indexing.py | def _compute_enlarge_labels(self, locator, base_index):
"""Helper for _enlarge_axis, compute common labels and extra labels.
Returns:
nan_labels: The labels needs to be added
"""
# base_index_type can be pd.Index or pd.DatetimeIndex
# depending on user input and pan... | def _compute_enlarge_labels(self, locator, base_index):
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Returns:
nan_labels: The labels needs to be added
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train | _split_result_for_readers | Splits the DataFrame read into smaller DataFrames and handles all edge cases.
Args:
axis: Which axis to split over.
num_splits: The number of splits to create.
df: The DataFrame after it has been read.
Returns:
A list of pandas DataFrames. | modin/engines/ray/pandas_on_ray/io.py | def _split_result_for_readers(axis, num_splits, df): # pragma: no cover
"""Splits the DataFrame read into smaller DataFrames and handles all edge cases.
Args:
axis: Which axis to split over.
num_splits: The number of splits to create.
df: The DataFrame after it has been read.
Retu... | def _split_result_for_readers(axis, num_splits, df): # pragma: no cover
"""Splits the DataFrame read into smaller DataFrames and handles all edge cases.
Args:
axis: Which axis to split over.
num_splits: The number of splits to create.
df: The DataFrame after it has been read.
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train | _read_parquet_columns | Use a Ray task to read columns from Parquet into a Pandas DataFrame.
Note: Ray functions are not detected by codecov (thus pragma: no cover)
Args:
path: The path of the Parquet file.
columns: The list of column names to read.
num_splits: The number of partitions to split the column int... | modin/engines/ray/pandas_on_ray/io.py | def _read_parquet_columns(path, columns, num_splits, kwargs): # pragma: no cover
"""Use a Ray task to read columns from Parquet into a Pandas DataFrame.
Note: Ray functions are not detected by codecov (thus pragma: no cover)
Args:
path: The path of the Parquet file.
columns: The list of c... | def _read_parquet_columns(path, columns, num_splits, kwargs): # pragma: no cover
"""Use a Ray task to read columns from Parquet into a Pandas DataFrame.
Note: Ray functions are not detected by codecov (thus pragma: no cover)
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path: The path of the Parquet file.
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train | _read_csv_with_offset_pandas_on_ray | Use a Ray task to read a chunk of a CSV into a Pandas DataFrame.
Note: Ray functions are not detected by codecov (thus pragma: no cover)
Args:
fname: The filename of the file to open.
num_splits: The number of splits (partitions) to separate the DataFrame into.
start: The start byte of... | modin/engines/ray/pandas_on_ray/io.py | def _read_csv_with_offset_pandas_on_ray(
fname, num_splits, start, end, kwargs, header
): # pragma: no cover
"""Use a Ray task to read a chunk of a CSV into a Pandas DataFrame.
Note: Ray functions are not detected by codecov (thus pragma: no cover)
Args:
fname: The filename of the file to ope... | def _read_csv_with_offset_pandas_on_ray(
fname, num_splits, start, end, kwargs, header
): # pragma: no cover
"""Use a Ray task to read a chunk of a CSV into a Pandas DataFrame.
Note: Ray functions are not detected by codecov (thus pragma: no cover)
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train | _read_hdf_columns | Use a Ray task to read columns from HDF5 into a Pandas DataFrame.
Note: Ray functions are not detected by codecov (thus pragma: no cover)
Args:
path_or_buf: The path of the HDF5 file.
columns: The list of column names to read.
num_splits: The number of partitions to split the column in... | modin/engines/ray/pandas_on_ray/io.py | def _read_hdf_columns(path_or_buf, columns, num_splits, kwargs): # pragma: no cover
"""Use a Ray task to read columns from HDF5 into a Pandas DataFrame.
Note: Ray functions are not detected by codecov (thus pragma: no cover)
Args:
path_or_buf: The path of the HDF5 file.
columns: The list ... | def _read_hdf_columns(path_or_buf, columns, num_splits, kwargs): # pragma: no cover
"""Use a Ray task to read columns from HDF5 into a Pandas DataFrame.
Note: Ray functions are not detected by codecov (thus pragma: no cover)
Args:
path_or_buf: The path of the HDF5 file.
columns: The list ... | [
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train | _read_feather_columns | Use a Ray task to read columns from Feather into a Pandas DataFrame.
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Args:
path: The path of the Feather file.
columns: The list of column names to read.
num_splits: The number of partitions to split the column int... | modin/engines/ray/pandas_on_ray/io.py | def _read_feather_columns(path, columns, num_splits): # pragma: no cover
"""Use a Ray task to read columns from Feather into a Pandas DataFrame.
Note: Ray functions are not detected by codecov (thus pragma: no cover)
Args:
path: The path of the Feather file.
columns: The list of column na... | def _read_feather_columns(path, columns, num_splits): # pragma: no cover
"""Use a Ray task to read columns from Feather into a Pandas DataFrame.
Note: Ray functions are not detected by codecov (thus pragma: no cover)
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train | _read_sql_with_limit_offset | Use a Ray task to read a chunk of SQL source.
Note: Ray functions are not detected by codecov (thus pragma: no cover) | modin/engines/ray/pandas_on_ray/io.py | def _read_sql_with_limit_offset(
num_splits, sql, con, index_col, kwargs
): # pragma: no cover
"""Use a Ray task to read a chunk of SQL source.
Note: Ray functions are not detected by codecov (thus pragma: no cover)
"""
pandas_df = pandas.read_sql(sql, con, index_col=index_col, **kwargs)
if in... | def _read_sql_with_limit_offset(
num_splits, sql, con, index_col, kwargs
): # pragma: no cover
"""Use a Ray task to read a chunk of SQL source.
Note: Ray functions are not detected by codecov (thus pragma: no cover)
"""
pandas_df = pandas.read_sql(sql, con, index_col=index_col, **kwargs)
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train | get_index | Get the index from the indices returned by the workers.
Note: Ray functions are not detected by codecov (thus pragma: no cover) | modin/engines/ray/generic/io.py | def get_index(index_name, *partition_indices): # pragma: no cover
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Note: Ray functions are not detected by codecov (thus pragma: no cover)"""
index = partition_indices[0].append(partition_indices[1:])
index.names = index_name
return index | def get_index(index_name, *partition_indices): # pragma: no cover
"""Get the index from the indices returned by the workers.
Note: Ray functions are not detected by codecov (thus pragma: no cover)"""
index = partition_indices[0].append(partition_indices[1:])
index.names = index_name
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train | RayIO.read_parquet | Load a parquet object from the file path, returning a DataFrame.
Ray DataFrame only supports pyarrow engine for now.
Args:
path: The filepath of the parquet file.
We only support local files for now.
engine: Ray only support pyarrow reader.
... | modin/engines/ray/generic/io.py | def read_parquet(cls, path, engine, columns, **kwargs):
"""Load a parquet object from the file path, returning a DataFrame.
Ray DataFrame only supports pyarrow engine for now.
Args:
path: The filepath of the parquet file.
We only support local files for now.
... | def read_parquet(cls, path, engine, columns, **kwargs):
"""Load a parquet object from the file path, returning a DataFrame.
Ray DataFrame only supports pyarrow engine for now.
Args:
path: The filepath of the parquet file.
We only support local files for now.
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train | RayIO._read_csv_from_file_pandas_on_ray | Constructs a DataFrame from a CSV file.
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filepath (str): path to the CSV file.
npartitions (int): number of partitions for the DataFrame.
kwargs (dict): args excluding filepath provided to read_csv.
Returns:
DataFrame or Series constructed from CSV ... | modin/engines/ray/generic/io.py | def _read_csv_from_file_pandas_on_ray(cls, filepath, kwargs={}):
"""Constructs a DataFrame from a CSV file.
Args:
filepath (str): path to the CSV file.
npartitions (int): number of partitions for the DataFrame.
kwargs (dict): args excluding filepath provided to read_... | def _read_csv_from_file_pandas_on_ray(cls, filepath, kwargs={}):
"""Constructs a DataFrame from a CSV file.
Args:
filepath (str): path to the CSV file.
npartitions (int): number of partitions for the DataFrame.
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train | RayIO._read | Read csv file from local disk.
Args:
filepath_or_buffer:
The filepath of the csv file.
We only support local files for now.
kwargs: Keyword arguments in pandas.read_csv | modin/engines/ray/generic/io.py | def _read(cls, filepath_or_buffer, **kwargs):
"""Read csv file from local disk.
Args:
filepath_or_buffer:
The filepath of the csv file.
We only support local files for now.
kwargs: Keyword arguments in pandas.read_csv
"""
# The ... | def _read(cls, filepath_or_buffer, **kwargs):
"""Read csv file from local disk.
Args:
filepath_or_buffer:
The filepath of the csv file.
We only support local files for now.
kwargs: Keyword arguments in pandas.read_csv
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train | RayIO.read_hdf | Load a h5 file from the file path or buffer, returning a DataFrame.
Args:
path_or_buf: string, buffer or path object
Path to the file to open, or an open :class:`pandas.HDFStore` object.
kwargs: Pass into pandas.read_hdf function.
Returns:
DataFrame ... | modin/engines/ray/generic/io.py | def read_hdf(cls, path_or_buf, **kwargs):
"""Load a h5 file from the file path or buffer, returning a DataFrame.
Args:
path_or_buf: string, buffer or path object
Path to the file to open, or an open :class:`pandas.HDFStore` object.
kwargs: Pass into pandas.read_h... | def read_hdf(cls, path_or_buf, **kwargs):
"""Load a h5 file from the file path or buffer, returning a DataFrame.
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path_or_buf: string, buffer or path object
Path to the file to open, or an open :class:`pandas.HDFStore` object.
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train | RayIO.read_feather | Read a pandas.DataFrame from Feather format.
Ray DataFrame only supports pyarrow engine for now.
Args:
path: The filepath of the feather file.
We only support local files for now.
multi threading is set to True by default
columns: not support... | modin/engines/ray/generic/io.py | def read_feather(cls, path, columns=None, use_threads=True):
"""Read a pandas.DataFrame from Feather format.
Ray DataFrame only supports pyarrow engine for now.
Args:
path: The filepath of the feather file.
We only support local files for now.
mu... | def read_feather(cls, path, columns=None, use_threads=True):
"""Read a pandas.DataFrame from Feather format.
Ray DataFrame only supports pyarrow engine for now.
Args:
path: The filepath of the feather file.
We only support local files for now.
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train | RayIO.to_sql | Write records stored in a DataFrame to a SQL database.
Args:
qc: the query compiler of the DF that we want to run to_sql on
kwargs: parameters for pandas.to_sql(**kwargs) | modin/engines/ray/generic/io.py | def to_sql(cls, qc, **kwargs):
"""Write records stored in a DataFrame to a SQL database.
Args:
qc: the query compiler of the DF that we want to run to_sql on
kwargs: parameters for pandas.to_sql(**kwargs)
"""
# we first insert an empty DF in order to create the fu... | def to_sql(cls, qc, **kwargs):
"""Write records stored in a DataFrame to a SQL database.
Args:
qc: the query compiler of the DF that we want to run to_sql on
kwargs: parameters for pandas.to_sql(**kwargs)
"""
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train | RayIO.read_sql | Reads a SQL query or database table into a DataFrame.
Args:
sql: string or SQLAlchemy Selectable (select or text object) SQL query to be
executed or a table name.
con: SQLAlchemy connectable (engine/connection) or database string URI or
DBAPI2 connection (... | modin/engines/ray/generic/io.py | def read_sql(cls, sql, con, index_col=None, **kwargs):
"""Reads a SQL query or database table into a DataFrame.
Args:
sql: string or SQLAlchemy Selectable (select or text object) SQL query to be
executed or a table name.
con: SQLAlchemy connectable (engine/connect... | def read_sql(cls, sql, con, index_col=None, **kwargs):
"""Reads a SQL query or database table into a DataFrame.
Args:
sql: string or SQLAlchemy Selectable (select or text object) SQL query to be
executed or a table name.
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train | to_datetime | Convert the arg to datetime format. If not Ray DataFrame, this falls
back on pandas.
Args:
errors ('raise' or 'ignore'): If 'ignore', errors are silenced.
Pandas blatantly ignores this argument so we will too.
dayfirst (bool): Date format is passed in as day first.
yearfi... | modin/pandas/datetimes.py | def to_datetime(
arg,
errors="raise",
dayfirst=False,
yearfirst=False,
utc=None,
box=True,
format=None,
exact=True,
unit=None,
infer_datetime_format=False,
origin="unix",
cache=False,
):
"""Convert the arg to datetime format. If not Ray DataFrame, this falls
ba... | def to_datetime(
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yearfirst=False,
utc=None,
box=True,
format=None,
exact=True,
unit=None,
infer_datetime_format=False,
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cache=False,
):
"""Convert the arg to datetime format. If not Ray DataFrame, this falls
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train | read_sql | Read SQL query or database table into a DataFrame.
Args:
sql: string or SQLAlchemy Selectable (select or text object) SQL query to be executed or a table name.
con: SQLAlchemy connectable (engine/connection) or database string URI or DBAPI2 connection (fallback mode)
index_col: Column(s) to... | modin/experimental/pandas/io_exp.py | def read_sql(
sql,
con,
index_col=None,
coerce_float=True,
params=None,
parse_dates=None,
columns=None,
chunksize=None,
partition_column=None,
lower_bound=None,
upper_bound=None,
max_sessions=None,
):
""" Read SQL query or database table into a DataFrame.
Args:
... | def read_sql(
sql,
con,
index_col=None,
coerce_float=True,
params=None,
parse_dates=None,
columns=None,
chunksize=None,
partition_column=None,
lower_bound=None,
upper_bound=None,
max_sessions=None,
):
""" Read SQL query or database table into a DataFrame.
Args:
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train | RayFrameManager.block_lengths | Gets the lengths of the blocks.
Note: This works with the property structure `_lengths_cache` to avoid
having to recompute these values each time they are needed. | modin/engines/ray/generic/frame/partition_manager.py | def block_lengths(self):
"""Gets the lengths of the blocks.
Note: This works with the property structure `_lengths_cache` to avoid
having to recompute these values each time they are needed.
"""
if self._lengths_cache is None:
try:
# The first col... | def block_lengths(self):
"""Gets the lengths of the blocks.
Note: This works with the property structure `_lengths_cache` to avoid
having to recompute these values each time they are needed.
"""
if self._lengths_cache is None:
try:
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train | RayFrameManager.block_widths | Gets the widths of the blocks.
Note: This works with the property structure `_widths_cache` to avoid
having to recompute these values each time they are needed. | modin/engines/ray/generic/frame/partition_manager.py | def block_widths(self):
"""Gets the widths of the blocks.
Note: This works with the property structure `_widths_cache` to avoid
having to recompute these values each time they are needed.
"""
if self._widths_cache is None:
try:
# The first column ... | def block_widths(self):
"""Gets the widths of the blocks.
Note: This works with the property structure `_widths_cache` to avoid
having to recompute these values each time they are needed.
"""
if self._widths_cache is None:
try:
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train | deploy_ray_func | Deploy a function to a partition in Ray.
Note: Ray functions are not detected by codecov (thus pragma: no cover)
Args:
func: The function to apply.
partition: The partition to apply the function to.
kwargs: A dictionary of keyword arguments for the function.
Returns:
The r... | modin/engines/ray/pandas_on_ray/frame/partition.py | def deploy_ray_func(func, partition, kwargs): # pragma: no cover
"""Deploy a function to a partition in Ray.
Note: Ray functions are not detected by codecov (thus pragma: no cover)
Args:
func: The function to apply.
partition: The partition to apply the function to.
kwargs: A dict... | def deploy_ray_func(func, partition, kwargs): # pragma: no cover
"""Deploy a function to a partition in Ray.
Note: Ray functions are not detected by codecov (thus pragma: no cover)
Args:
func: The function to apply.
partition: The partition to apply the function to.
kwargs: A dict... | [
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train | PandasOnRayFramePartition.get | Gets the object out of the plasma store.
Returns:
The object from the plasma store. | modin/engines/ray/pandas_on_ray/frame/partition.py | def get(self):
"""Gets the object out of the plasma store.
Returns:
The object from the plasma store.
"""
if len(self.call_queue):
return self.apply(lambda x: x).get()
try:
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"""Gets the object out of the plasma store.
Returns:
The object from the plasma store.
"""
if len(self.call_queue):
return self.apply(lambda x: x).get()
try:
return ray.get(self.oid)
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Note: This works with the property structure `_widths_cache` to avoid
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train | BaseFrameManager.map_across_blocks | Applies `map_func` to every partition.
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map_func: The function to apply.
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"""Applies `map_func` to every partition.
Args:
map_func: The function to apply.
Returns:
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A new BaseFrameManager object, the type of object that called this.
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train | BaseFrameManager.copartition_datasets | Copartition two BlockPartitions objects.
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axis: The axis to copartition.
other: The other BlockPartitions object to copartition with.
left_func: The function to apply to left. If None, just use the dimension
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right_fun... | modin/engines/base/frame/partition_manager.py | def copartition_datasets(self, axis, other, left_func, right_func):
"""Copartition two BlockPartitions objects.
Args:
axis: The axis to copartition.
other: The other BlockPartitions object to copartition with.
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axis: The axis to copartition.
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train | BaseFrameManager.map_across_full_axis | Applies `map_func` to every partition.
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Args:
axis: The axis to perform the map across (0 - index, 1 - columns).
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Note: This method should be used in the case that `map_func` relies on
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train | BaseFrameManager.take | Take the first (or last) n rows or columns from the blocks
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Axis = 1 will be equivalent to `front` or `back`
Args:
axis: The axis to extract (0 for extracting rows, 1 for extracting columns)
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train | BaseFrameManager.concat | Concatenate the blocks with another set of blocks.
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condition is not met.
Args:
axis: The axis to concatenate to.
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train | BaseFrameManager.to_pandas | Convert this object into a Pandas DataFrame from the partitions.
Args:
is_transposed: A flag for telling this object that the external
representation is transposed, but not the internal.
Returns:
A Pandas DataFrame | modin/engines/base/frame/partition_manager.py | def to_pandas(self, is_transposed=False):
"""Convert this object into a Pandas DataFrame from the partitions.
Args:
is_transposed: A flag for telling this object that the external
representation is transposed, but not the internal.
Returns:
A Pandas Data... | def to_pandas(self, is_transposed=False):
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is_transposed: A flag for telling this object that the external
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train | BaseFrameManager.get_indices | This gets the internal indices stored in the partitions.
Note: These are the global indices of the object. This is mostly useful
when you have deleted rows/columns internally, but do not know
which ones were deleted.
Args:
axis: This axis to extract the labels. (0 -... | modin/engines/base/frame/partition_manager.py | def get_indices(self, axis=0, index_func=None, old_blocks=None):
"""This gets the internal indices stored in the partitions.
Note: These are the global indices of the object. This is mostly useful
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which ones were del... | def get_indices(self, axis=0, index_func=None, old_blocks=None):
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train | BaseFrameManager._get_blocks_containing_index | Convert a global index to a block index and local index.
Note: This method is primarily used to convert a global index into a
partition index (along the axis provided) and local index (useful
for `iloc` or similar operations.
Args:
axis: The axis along which to get ... | modin/engines/base/frame/partition_manager.py | def _get_blocks_containing_index(self, axis, index):
"""Convert a global index to a block index and local index.
Note: This method is primarily used to convert a global index into a
partition index (along the axis provided) and local index (useful
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train | BaseFrameManager._get_dict_of_block_index | Convert indices to a dict of block index to internal index mapping.
Note: See `_get_blocks_containing_index` for primary usage. This method
accepts a list of indices rather than just a single value, and uses
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Args:
axis: The axis along... | modin/engines/base/frame/partition_manager.py | def _get_dict_of_block_index(self, axis, indices, ordered=False):
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Note: See `_get_blocks_containing_index` for primary usage. This method
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`_... | def _get_dict_of_block_index(self, axis, indices, ordered=False):
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train | BaseFrameManager._apply_func_to_list_of_partitions | Applies a function to a list of remote partitions.
Note: The main use for this is to preprocess the func.
Args:
func: The func to apply
partitions: The list of partitions
Returns:
A list of BaseFramePartition objects. | modin/engines/base/frame/partition_manager.py | def _apply_func_to_list_of_partitions(self, func, partitions, **kwargs):
"""Applies a function to a list of remote partitions.
Note: The main use for this is to preprocess the func.
Args:
func: The func to apply
partitions: The list of partitions
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... | def _apply_func_to_list_of_partitions(self, func, partitions, **kwargs):
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Note: The main use for this is to preprocess the func.
Args:
func: The func to apply
partitions: The list of partitions
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train | BaseFrameManager.apply_func_to_select_indices | Applies a function to select indices.
Note: Your internal function must take a kwarg `internal_indices` for
this to work correctly. This prevents information leakage of the
internal index to the external representation.
Args:
axis: The axis to apply the func over.
... | modin/engines/base/frame/partition_manager.py | def apply_func_to_select_indices(self, axis, func, indices, keep_remaining=False):
"""Applies a function to select indices.
Note: Your internal function must take a kwarg `internal_indices` for
this to work correctly. This prevents information leakage of the
internal index to th... | def apply_func_to_select_indices(self, axis, func, indices, keep_remaining=False):
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Note: Your internal function must take a kwarg `internal_indices` for
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train | BaseFrameManager.apply_func_to_select_indices_along_full_axis | Applies a function to a select subset of full columns/rows.
Note: This should be used when you need to apply a function that relies
on some global information for the entire column/row, but only need
to apply a function to a subset.
Important: For your func to operate directly ... | modin/engines/base/frame/partition_manager.py | def apply_func_to_select_indices_along_full_axis(
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):
"""Applies a function to a select subset of full columns/rows.
Note: This should be used when you need to apply a function that relies
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train | BaseFrameManager.apply_func_to_indices_both_axis | Apply a function to along both axis
Important: For your func to operate directly on the indices provided,
it must use `row_internal_indices, col_internal_indices` as keyword
arguments. | modin/engines/base/frame/partition_manager.py | def apply_func_to_indices_both_axis(
self,
func,
row_indices,
col_indices,
lazy=False,
keep_remaining=True,
mutate=False,
item_to_distribute=None,
):
"""
Apply a function to along both axis
Important: For your func to operate d... | def apply_func_to_indices_both_axis(
self,
func,
row_indices,
col_indices,
lazy=False,
keep_remaining=True,
mutate=False,
item_to_distribute=None,
):
"""
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train | BaseFrameManager.inter_data_operation | Apply a function that requires two BaseFrameManager objects.
Args:
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func: The function to apply
other: The other BaseFrameManager object to apply func to.
Returns:
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"""Apply a function that requires two BaseFrameManager objects.
Args:
axis: The axis to apply the function over (0 - rows, 1 - columns)
func: The function to apply
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train | BaseFrameManager.manual_shuffle | Shuffle the partitions based on the `shuffle_func`.
Args:
axis: The axis to shuffle across.
shuffle_func: The function to apply before splitting the result.
lengths: The length of each partition to split the result into.
Returns:
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shuffle_func: The function to apply before splitting the result.
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train | read_parquet | Load a parquet object from the file path, returning a DataFrame.
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path: The filepath of the parquet file.
We only support local files for now.
engine: This argument doesn't do anything for now.
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"""Load a parquet object from the file path, returning a DataFrame.
Args:
path: The filepath of the parquet file.
We only support local files for now.
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path: The filepath of the parquet file.
We only support local files for now.
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sep: The separator default to use for the parser.
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train | _read | Read csv file from local disk.
Args:
filepath_or_buffer:
The filepath of the csv file.
We only support local files for now.
kwargs: Keyword arguments in pandas.read_csv | modin/pandas/io.py | def _read(**kwargs):
"""Read csv file from local disk.
Args:
filepath_or_buffer:
The filepath of the csv file.
We only support local files for now.
kwargs: Keyword arguments in pandas.read_csv
"""
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filepath_or_buffer:
The filepath of the csv file.
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kwargs: Keyword arguments in pandas.read_csv
"""
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Args:
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con: SQLAlchemy connectable (engine/connection) or database string URI or DBAPI2 connection (fallback mode)
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columns=None,
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train | BaseIO.read_parquet | Load a parquet object from the file path, returning a DataFrame.
Ray DataFrame only supports pyarrow engine for now.
Args:
path: The filepath of the parquet file.
We only support local files for now.
engine: Ray only support pyarrow reader.
... | modin/engines/base/io.py | def read_parquet(cls, path, engine, columns, **kwargs):
"""Load a parquet object from the file path, returning a DataFrame.
Ray DataFrame only supports pyarrow engine for now.
Args:
path: The filepath of the parquet file.
We only support local files for now.
... | def read_parquet(cls, path, engine, columns, **kwargs):
"""Load a parquet object from the file path, returning a DataFrame.
Ray DataFrame only supports pyarrow engine for now.
Args:
path: The filepath of the parquet file.
We only support local files for now.
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filepath_or_buffer:
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We only support local files for now.
kwargs: Keyword arguments in pandas.read_csv
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kwargs: Keyword arguments in pandas.read_csv
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train | auto_select_categorical_features | Make a feature mask of categorical features in X.
Features with less than 10 unique values are considered categorical.
Parameters
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X : array-like or sparse matrix, shape=(n_samples, n_features)
Dense array or sparse matrix.
threshold : int
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Features with less than 10 unique values are considered categorical.
Parameters
----------
X : array-like or sparse matrix, shape=(n_samples, n_features)
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Features with less than 10 unique values are considered categorical.
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----------
X : array-like or sparse matrix, shape=(n_samples, n_features)
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train | _X_selected | Split X into selected features and other features | tpot/builtins/one_hot_encoder.py | def _X_selected(X, selected):
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Dense array or sparse matrix.
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X : array-like or sparse matrix, shape=(n_samples, n_features)
Dense array or sparse matrix.
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Parameters
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X : array-like, shape=(n_samples, n_feature)
Input array of type int.
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train | OneHotEncoder._fit_transform | Assume X contains only categorical features.
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X : array-like or sparse matrix, shape=(n_samples, n_features)
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X : array-like or sparse matrix, shape=(n_samples, n_features)
Dense array or sparse matrix.
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X = self._matrix_adjust(X)
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X : array-like or sparse matrix, shape=(n_samples, n_features)
Dense array or sparse matrix.
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X = self._matrix_adjust(X)
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train | OneHotEncoder.fit_transform | Fit OneHotEncoder to X, then transform X.
Equivalent to self.fit(X).transform(X), but more convenient and more
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Parameters
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X : array-like or sparse matrix, shape=(n_samples, n_features)
... | tpot/builtins/one_hot_encoder.py | def fit_transform(self, X, y=None):
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Parameters
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train | OneHotEncoder._transform | Asssume X contains only categorical features.
Parameters
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X : array-like or sparse matrix, shape=(n_samples, n_features)
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X : array-like or sparse matrix, shape=(n_samples, n_features)
Dense array or sparse matrix.
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X = self._matrix_adjust(X)
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Dense array or sparse matrix.
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train | OneHotEncoder.transform | Transform X using one-hot encoding.
Parameters
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X : array-like or sparse matrix, shape=(n_samples, n_features)
Dense array or sparse matrix.
Returns
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X_out : sparse matrix if sparse=True else a 2-d array, dtype=int
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"""Transform X using one-hot encoding.
Parameters
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X : array-like or sparse matrix, shape=(n_samples, n_features)
Dense array or sparse matrix.
Returns
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X : array-like or sparse matrix, shape=(n_samples, n_features)
Dense array or sparse matrix.
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train | TPOTBase.fit | Fit an optimized machine learning pipeline.
Uses genetic programming to optimize a machine learning pipeline that
maximizes score on the provided features and target. Performs internal
k-fold cross-validaton to avoid overfitting on the provided data. The
best pipeline is then trained on... | tpot/base.py | def fit(self, features, target, sample_weight=None, groups=None):
"""Fit an optimized machine learning pipeline.
Uses genetic programming to optimize a machine learning pipeline that
maximizes score on the provided features and target. Performs internal
k-fold cross-validaton to avoid o... | def fit(self, features, target, sample_weight=None, groups=None):
"""Fit an optimized machine learning pipeline.
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train | TPOTBase._setup_memory | Setup Memory object for memory caching. | tpot/base.py | def _setup_memory(self):
"""Setup Memory object for memory caching.
"""
if self.memory:
if isinstance(self.memory, str):
if self.memory == "auto":
# Create a temporary folder to store the transformers of the pipeline
self._cache... | def _setup_memory(self):
"""Setup Memory object for memory caching.
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# Create a temporary folder to store the transformers of the pipeline
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train | TPOTBase._update_top_pipeline | Helper function to update the _optimized_pipeline field. | tpot/base.py | def _update_top_pipeline(self):
"""Helper function to update the _optimized_pipeline field."""
# Store the pipeline with the highest internal testing score
if self._pareto_front:
self._optimized_pipeline_score = -float('inf')
for pipeline, pipeline_scores in zip(self._par... | def _update_top_pipeline(self):
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# Store the pipeline with the highest internal testing score
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train | TPOTBase._summary_of_best_pipeline | Print out best pipeline at the end of optimization process.
Parameters
----------
features: array-like {n_samples, n_features}
Feature matrix
target: array-like {n_samples}
List of class labels for prediction
Returns
-------
self: object... | tpot/base.py | def _summary_of_best_pipeline(self, features, target):
"""Print out best pipeline at the end of optimization process.
Parameters
----------
features: array-like {n_samples, n_features}
Feature matrix
target: array-like {n_samples}
List of class labels fo... | def _summary_of_best_pipeline(self, features, target):
"""Print out best pipeline at the end of optimization process.
Parameters
----------
features: array-like {n_samples, n_features}
Feature matrix
target: array-like {n_samples}
List of class labels fo... | [
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... | b626271e6b5896a73fb9d7d29bebc7aa9100772e |
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