Implement yli.auto_correlations

yli/__init__.py

@@ -16,7 +16,7 @@
 
 from .bayes_factors import bayesfactor_afbf
 from .config import config
-from .descriptives import auto_descriptives
+from .descriptives import auto_correlations, auto_descriptives
 from .distributions import beta_oddsratio, beta_ratio, hdi, transformed_dist
 from .io import pickle_read_compressed, pickle_read_encrypted, pickle_write_compressed, pickle_write_encrypted
 from .regress import OrdinalLogit, PenalisedLogit, logit_then_regress, regress, vif
@@ -33,7 +33,7 @@ def reload_me():
 				try:
 					importlib.reload(v)
 				except ModuleNotFoundError as ex:
-					if ex.name.startswith('yli.'):
+					if ex.name == k:
 						# Must be due to a module which we deleted - can safely ignore
 						pass
 					else:

yli/descriptives.py

@@ -15,9 +15,11 @@
 #   along with this program.  If not, see <https://www.gnu.org/licenses/>.
 
 import pandas as pd
+from scipy import stats
+import seaborn as sns
 
 from .config import config
-from .utils import check_nan
+from .utils import as_numeric, check_nan
 
 def auto_descriptives(df, cols, *, ordinal_range=[]):
 	"""
@@ -140,3 +142,107 @@ class AutoDescriptivesResult:
 		result_labels_fmt = [r[0] for r in self._result_labels]
 		table = pd.DataFrame(self._result_data, index=result_labels_fmt, columns=['', 'Missing'])
 		return str(table)
+
+def auto_correlations(df, cols):
+	# TODO: Documentation
+	
+	def _col_to_numeric(col):
+		if col.dtype == 'category' and col.cat.ordered:
+			# Ordinal variable
+			# Factorise if required
+			col, _ = as_numeric(col)
+			
+			# Code as ranks
+			col[col >= 0] = stats.rankdata(col[col >= 0])
+			
+			# Put NaNs back
+			col = col.astype('float64')
+			col[col < 0] = pd.NA
+			
+			return col
+		else:
+			# FIXME: Bools, binary, etc.
+			return col
+	
+	# Code columns as numeric/ranks/etc. as appropriate
+	df_coded = pd.DataFrame(index=df.index)
+
+	for col_name in cols:
+		col = df[col_name]
+		
+		if col.dtype == 'category' and col.cat.ordered:
+			# Ordinal variable
+			# Factorise if required
+			col, _ = as_numeric(col)
+			
+			# Code as ranks
+			col[col >= 0] = stats.rankdata(col[col >= 0])
+			
+			# Put NaNs back
+			col = col.astype('float64')
+			col[col < 0] = pd.NA
+			
+			df_coded[col_name] = col
+		elif col.dtype in ('bool', 'boolean', 'category', 'object'):
+			cat_values = col.dropna().unique()
+			
+			if len(cat_values) == 2:
+				# Categorical variable with 2 categories
+				# Code as 0/1/NA
+				cat_values = sorted(cat_values)
+				col = col.replace({cat_values[0]: 0, cat_values[1]: 1})
+				df_coded[col_name] = col
+			else:
+				# Categorical variable with >2 categories
+				# Create dummy variables
+				dummies = pd.get_dummies(col, prefix=col_name)
+				df_coded = df_coded.join(dummies)
+		else:
+			# Numeric variable, etc.
+			df_coded[col_name] = col
+	
+	# Compute pairwise correlation
+	df_corr = pd.DataFrame(index=df_coded.columns, columns=df_coded.columns, dtype='float64')
+	
+	for i, col1 in enumerate(df_coded.columns):
+		for col2 in df_coded.columns[:i]:
+			statistic = stats.pearsonr(df_coded[col1], df_coded[col2]).statistic
+			df_corr.loc[col1, col2] = statistic
+			df_corr.loc[col2, col1] = statistic
+		
+		# Correlation with itself is always 1
+		df_corr.loc[col1, col1] = 1
+	
+	return AutoCorrelationsResult(df_corr)
+
+class AutoCorrelationsResult:
+	# TODO: Documentation
+	
+	def __init__(self, correlations):
+		self.correlations = correlations
+	
+	def __repr__(self):
+		if config.repr_is_summary:
+			return self.summary()
+		return super().__repr__()
+	
+	def _repr_html_(self):
+		df_repr = self.correlations._repr_html_()
+		
+		# Insert caption
+		idx_endopen = df_repr.index('>', df_repr.index('<table'))
+		df_repr = df_repr[:idx_endopen+1] + '<caption>Correlation Matrix</caption>' + df_repr[idx_endopen+1:]
+		
+		return df_repr
+	
+	def summary(self):
+		"""
+		Return a stringified summary of the correlation matrix
+		
+		:rtype: str
+		"""
+		
+		return 'Correlation Matrix\n\n' + str(self.correlations)
+	
+	def plot(self):
+		sns.heatmap(self.correlations, vmin=-1, vmax=1, cmap='RdBu')