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Finalising corr's docstring #751

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Oct 18, 2023
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Finalising corr's docstring #751

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2999554
Finalising corr's docstring
oualib Oct 17, 2023
bced15d
black fix
mail4umar Oct 18, 2023
fc6ca30
Update _corr.py
mail4umar Oct 18, 2023
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197 changes: 182 additions & 15 deletions verticapy/core/vdataframe/_corr.py
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Original file line number Diff line number Diff line change
Expand Up @@ -862,6 +862,24 @@ def corr(
.. raw:: html
:file: SPHINX_DIRECTORY/figures/core_vDataFrame_vDFCorr_corr_matrix.html

You can also use the parameter focus to only compute a correlation vector.

.. code-block:: python

data.corr(method = "pearson", focus = "score1")

.. ipython:: python
:suppress:

fig = data.corr(method = "pearson", focus = "score1")
fig.write_html("figures/core_vDataFrame_vDFCorr_corr_vector.html")

.. raw:: html
:file: SPHINX_DIRECTORY/figures/core_vDataFrame_vDFCorr_corr_vector.html

It is less expensive and it allows you to focus your search on one specific
column.

For more examples, please look at the :ref:`chart_gallery.corr` page of the
:ref:`chart_gallery`.

Expand Down Expand Up @@ -904,8 +922,11 @@ def corr_pvalue(
] = "pearson",
) -> tuple[float, float]:
"""
Computes the Correlation Coefficient of the two input
vDataColumns and its pvalue.
Computes the Correlation Coefficient between two input
vDataColumns, along with its associated p-value. This
calculation helps assess the strength and direction
of the relationship between the two columns and provides
statistical significance through the p-value.

Parameters
----------
Expand Down Expand Up @@ -1130,7 +1151,12 @@ def cov(
**style_kwargs,
) -> PlottingObject:
"""
Computes the covariance matrix of the vDataFrame.
Computes the covariance matrix of the vDataFrame. This matrix
summarizes the covariances between pairs of variables in the
dataset, shedding light on how variables move in relation to
each other. It's an important tool in understanding the
relationships and interactions between variables, which can
be used for various statistical analyses and modeling tasks.

Parameters
----------
Expand Down Expand Up @@ -1205,6 +1231,24 @@ def cov(
.. raw:: html
:file: SPHINX_DIRECTORY/figures/core_vDataFrame_vDFCorr_cov_matrix.html

You can also use the parameter focus to only compute a covariance vector.

.. code-block:: python

data.cov(method = "pearson", focus = "score1")

.. ipython:: python
:suppress:

fig = data.cov(method = "pearson", focus = "score1")
fig.write_html("figures/core_vDataFrame_vDFCorr_cov_vector.html")

.. raw:: html
:file: SPHINX_DIRECTORY/figures/core_vDataFrame_vDFCorr_cov_vector.html

It is less expensive and it allows you to focus your search on one specific
column.

For more examples, please look at the :ref:`chart_gallery.corr` page of the
:ref:`chart_gallery`. Those ones are related to correlation matrix, but the
customization stays the same for the covariance matrix.
Expand Down Expand Up @@ -1253,7 +1297,12 @@ def regr(
**style_kwargs,
) -> PlottingObject:
"""
Computes the regression matrix of the vDataFrame.
Calculates the regression matrix for the given vDataFrame.
This matrix is essential in regression analysis, enabling
the modeling of relationships between variables and predicting
outcomes. It plays a crucial role in understanding how
independent variables influence the dependent variable, which
can be invaluable for various data analysis and modeling tasks.

Parameters
----------
Expand Down Expand Up @@ -1535,8 +1584,12 @@ def acf(
**style_kwargs,
) -> PlottingObject:
"""
Computes the correlations of the input vDataColumn
and its lags.
Calculates the correlations between the specified vDataColumn
and its various time lags. This function is particularly useful
for time series analysis and forecasting as it helps uncover
relationships between data points at different time intervals.
Understanding these correlations can be vital for making
predictions and gaining insights into temporal data patterns.

Parameters
----------
Expand Down Expand Up @@ -1625,6 +1678,9 @@ def acf(

data = load_amazon()

.. raw:: html
:file: SPHINX_DIRECTORY/figures/datasets_loaders_load_amazon.html

Draw the ACF Plot.

.. code-block:: python
Expand Down Expand Up @@ -1754,8 +1810,13 @@ def pacf(
**style_kwargs,
):
"""
Computes the partial autocorrelations of the input
vDataColumn.
Computes the partial autocorrelations of the specified vDataColumn.
Partial autocorrelations are a fundamental concept in time series
analysis and provide essential information about the dependencies
between data points at different time lags. Understanding these
partial autocorrelations can aid in modeling and predicting future
values, making it a valuable tool for time series analysis and
forecasting.

Parameters
----------
Expand Down Expand Up @@ -1839,6 +1900,9 @@ def pacf(

data = load_amazon()

.. raw:: html
:file: SPHINX_DIRECTORY/figures/datasets_loaders_load_amazon.html

Draw the PACF Plot.

.. code-block:: python
Expand Down Expand Up @@ -1996,9 +2060,11 @@ def iv_woe(
**style_kwargs,
) -> PlottingObject:
"""
Computes the Information Value (IV) Table. This shows
the predictive power of an independent variable in
relation to the dependent variable.
Calculates the Information Value (IV) Table, a powerful tool
for assessing the predictive capability of an independent
variable concerning a dependent variable. The IV Table
provides insights into how well the independent variable can
predict or explain variations in the dependent variable.

Parameters
----------
Expand All @@ -2023,6 +2089,57 @@ def iv_woe(
-------
obj
Plotting Object.

Examples
--------
Import the titanic dataset from `VerticaPy`.

.. code-block:: python

from verticapy.datasets import load_titanic

data = load_titanic()

.. raw:: html
:file: SPHINX_DIRECTORY/figures/datasets_loaders_load_titanic.html

Draw the IV Bar chart.

.. code-block:: python

data.iv_woe(y = "survived", nbins = 20)

.. ipython:: python
:suppress:

import verticapy as vp
from verticapy.datasets import load_titanic
vp.set_option("plotting_lib", "plotly")
data = load_titanic()
fig = data.iv_woe(y = "survived", nbins = 20)
fig.write_html("figures/core_vDataFrame_vDFCorr_iv_woe_plot.html")

.. raw:: html
:file: SPHINX_DIRECTORY/figures/core_vDataFrame_vDFCorr_iv_woe_plot.html

.. hint::

IV (Information Value) and WOE (Weight of Evidence) serve as powerful
tools for identifying factors that influence a response column without
the need to construct a full-fledged machine learning model.
These statistical metrics provide valuable insights into the predictive
power of independent variables concerning the dependent variable, aiding
in data analysis and decision-making processes.

Clearly, the factors that significantly influenced the survival of the
passengers were whether they had access to a lifeboat, their gender
(women and children were prioritized), and their class (passengers in
first class had a higher chance of evacuation). These observations
underscore the importance of these variables in predicting survival
outcomes during the Titanic disaster.

.. seealso::
| :py:mod:`verticapy.vDataColumn.iv_woe` : Computes IV / WOE table.
"""
columns = format_type(columns, dtype=list)
columns, y = self.format_colnames(columns, y)
Expand Down Expand Up @@ -2059,10 +2176,16 @@ class vDCCorr(vDCEncode):
@save_verticapy_logs
def iv_woe(self, y: str, nbins: int = 10) -> TableSample:
"""
Computes the Information Value (IV) / Weight Of
Evidence (WOE) Table. This shows the predictive
power of an independent variable in relation to
the dependent variable.
Calculates the Information Value (IV) / Weight Of
Evidence (WOE) Table. This table illustrates the
predictive strength of an independent variable
concerning the dependent variable. It provides a
measure of how well the independent variable can
predict or explain variations in the dependent
variable. The WOE values help quantify the
relationship between the independent and dependent
variables, offering valuable insights for predictive
modeling.

Parameters
----------
Expand All @@ -2076,6 +2199,50 @@ def iv_woe(self, y: str, nbins: int = 10) -> TableSample:
-------
obj
Tablesample.

Examples
--------
Import the titanic dataset from `VerticaPy`.

.. code-block:: python

from verticapy.datasets import load_titanic

data = load_titanic()

.. raw:: html
:file: SPHINX_DIRECTORY/figures/datasets_loaders_load_titanic.html

Draw the IV Bar chart.

.. code-block:: python

data["age"].iv_woe(y = "survived", nbins = 20)

.. ipython:: python
:suppress:

from verticapy.datasets import load_titanic
data = load_titanic()
html_file = open("figures/core_vDataFrame_vDFCorr_iv_woe_table.html", "w")
html_file.write(data["age"].iv_woe(y = "survived", nbins = 20)._repr_html_())
html_file.close()

.. raw:: html
:file: SPHINX_DIRECTORY/figures/core_vDataFrame_vDFCorr_iv_woe_table.html

.. hint::

The IV/WOE Table plays a pivotal role in calculating the global
Information Value (IV). This global IV serves as a valuable
indicator for identifying the predictors most strongly correlated
with a response column, enabling the discovery of key relationships
without the necessity of constructing a comprehensive machine learning
model. It's a powerful tool for efficient data analysis and decision
-making.

.. seealso::
| :py:mod:`verticapy.vDataFrame.iv_woe` : Draw IV Plot.
"""
y = self._parent.format_colnames(y)
assert self._parent[y].nunique() == 2, TypeError(
Expand Down