class_docstr: Significance of correlation of all variable combinations in the DataFrame.
  
  See :py:meth:`~phik.significance.significance_matrix` for more details.
  This analysis requires :py:class:`~autogluon.eda.analysis.interaction.Correlation` results to be
  available in the state.
  
  Note: it is recommended to apply AutoGluon standard pre-processing - this will allow to include categorical variables into the analysis.
  This can be done via wrapping analysis into :py:class:`~autogluon.eda.analysis.transform.ApplyFeatureGenerator`
  
  Examples
  --------
  >>> import autogluon.eda.analysis as eda
  >>> import autogluon.eda.visualization as viz
  >>> import autogluon.eda.auto as auto
  >>> import pandas as pd
  >>> df_train = pd.DataFrame(...)
  >>>
  >>> auto.analyze(return_sttrain_data=df_train, label=target_col, anlz_facets=[
  >>>     # Apply standard AutoGluon pre-processing to transform categorical variables to numbers to ensure correlation includes them.
  >>>     eda.transform.ApplyFeatureGenerator(category_to_numbers=True, children=[
  >>>         # We use `spearman` correlation to capture non-linear interactions because it is based on the order rank.
  >>>         eda.interaction.Correlation(method='spearman', focus_field=target_col, focus_field_threshold=0.3),
  >>>         eda.interaction.CorrelationSignificance()
  >>>     ])
  >>> ], viz_facets=[
  >>>     viz.interaction.CorrelationSignificanceVisualization(fig_args=dict(figsize=(12,8))),
  >>> ])
  
  See Also
  --------
  :py:meth:`~phik.significance.significance_matrix`
  :py:class:`~autogluon.eda.analysis.interaction.Correlation`
  :py:class:`~autogluon.eda.analysis.transform.ApplyFeatureGenerator`
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code: def _fit(
      self, state: AnalysisState, args: AnalysisState, **fit_kwargs
  ) -> None:
      state.significance_matrix = {}
      for ds, df in self.available_datasets(args):
          state.significance_matrix[ds] = df[
              state.correlations[ds].columns
          ].significance_matrix(**self.args, verbose=False)
  
imports: import warnings
  from typing import Any, Dict, List, Optional, Union
  import numpy
  import pandas
  import phik
  from pandas.core.dtypes.common import is_numeric_dtype
  from scipy import stats
  from scipy.cluster import hierarchy
  from scipy.stats import spearmanr
  from ..None import AnalysisState
  from .base import AbstractAnalysis
  from autogluon.common.features.types import R_FLOAT, R_INT
label_desc: Use image node_id 2 for calling the CorrelationSignificance obj's underlying member method code with example usage: obj._fit(state, args) without return types