def correlation_distance(self, how: str = 'euclidean') -> float:
"""
Calculate distance between correlation matrices with certain metric.
:param how: metric to measure distance. Choose from [``euclidean``, ``mae``, ``rmse``].
:return: distance between the association matrices in the chosen evaluation metric. Default: Euclidean
"""
from scipy.spatial.distance import cosine
if how == 'euclidean':
distance_func = euclidean_distance
elif how == 'mae':
distance_func = mean_absolute_error
elif how == 'rmse':
distance_func = rmse
elif how == 'cosine':
def custom_cosine(a, b):
return cosine(a.reshape(-1), b.reshape(-1))
distance_func = custom_cosine
else:
raise ValueError(
f'`how` parameter must be in [euclidean, mae, rmse]')
real_corr = compute_associations(
self.real, nominal_columns=self.categorical_columns, theil_u=True)
fake_corr = compute_associations(
self.fake, nominal_columns=self.categorical_columns, theil_u=True)
return distance_func(real_corr.values, fake_corr.values)
def test_associations():
"""
Tests that check wether the dython associations are still computed as is expected.
"""
# load test data
real_assoc = pd.read_csv(test_data_folder / 'real_associations.csv',
index_col='Unnamed: 0')
real_assoc_theil = pd.read_csv(test_data_folder /
'real_associations_theil.csv',
index_col='Unnamed: 0')
fake_assoc = pd.read_csv(test_data_folder / 'fake_associations.csv',
index_col='Unnamed: 0')
fake_assoc_theil = pd.read_csv(test_data_folder /
'fake_associations_theil.csv',
index_col='Unnamed: 0')
# Assert equality with saved data
pd.testing.assert_frame_equal(
real_assoc, compute_associations(real, nominal_columns=cat_cols))
pd.testing.assert_frame_equal(
real_assoc_theil,
compute_associations(real, nominal_columns=cat_cols, theil_u=True))
pd.testing.assert_frame_equal(
fake_assoc, compute_associations(fake, nominal_columns=cat_cols))
pd.testing.assert_frame_equal(
fake_assoc_theil,
compute_associations(fake, nominal_columns=cat_cols, theil_u=True))
def test_associations():
# load test data
real_assoc = pd.read_csv(test_data_folder/'real_associations.csv', index_col='Unnamed: 0')
real_assoc_theil = pd.read_csv(test_data_folder/'real_associations_theil.csv', index_col='Unnamed: 0')
fake_assoc = pd.read_csv(test_data_folder/'fake_associations.csv', index_col='Unnamed: 0')
fake_assoc_theil = pd.read_csv(test_data_folder/'fake_associations_theil.csv', index_col='Unnamed: 0')
# Assert equality with saved data
pd.testing.assert_frame_equal(real_assoc, compute_associations(real, nominal_columns=cat_cols))
pd.testing.assert_frame_equal(real_assoc_theil, compute_associations(real, nominal_columns=cat_cols, theil_u=True))
pd.testing.assert_frame_equal(fake_assoc, compute_associations(fake, nominal_columns=cat_cols))
pd.testing.assert_frame_equal(fake_assoc_theil, compute_associations(fake, nominal_columns=cat_cols, theil_u=True))
def correlation_correlation(self) -> float:
"""
Calculate the correlation coefficient between the association matrices of self.real and self.fake using self.comparison_metric
:return: The correlation coefficient
"""
total_metrics = pd.DataFrame()
for ds_name in ['real', 'fake']:
ds = getattr(self, ds_name)
corr_df = compute_associations(ds, nominal_columns=self.categorical_columns, theil_u=True)
values = corr_df.values
values = values[~np.eye(values.shape[0], dtype=bool)].reshape(values.shape[0], -1)
total_metrics[ds_name] = values.flatten()
self.correlation_correlations = total_metrics
corr, p = self.comparison_metric(total_metrics['real'], total_metrics['fake'])
if self.verbose:
print('\nColumn correlation between datasets:')
print(total_metrics.to_string())
return corr
idx = 0
fig, ax = plt.subplots(figsize = (4,4))
ax.scatter(s2[idx], s_full[idx])
plt.title(full_contra.columns[idx])
for i, txt in enumerate(full_contra.columns):
ax.annotate(txt, (s2[idx][i], s_full[idx][i]))
ax.set_xlim([-1,1])
ax.set_ylim([-1,1])
# Compare the representation between completed cosine similarity and original associations
associations(complete_y.astype(float), nominal_columns = cat_features)
associations(completed_y.astype(float), nominal_columns = cat_features)
associations(full_contra.astype(float), nominal_columns = cat_features)
assoc = compute_associations(full_contra.astype(float), nominal_columns = cat_features).values
idx = 0
fig, ax = plt.subplots(figsize = (4,4))
ax.scatter(assoc[idx], s_full[idx])
plt.title(full_contra.columns[idx])
for i, txt in enumerate(full_contra.columns):
ax.annotate(txt, (assoc[idx][i], s_full[idx][i]))
ax.set_xlim([-1,1])
ax.set_ylim([-1,1])
#======================================
# Ez.y
#======================================
if cat_features[col_idx]:
le = LabelEncoder()
data[dataset][colname] = le.fit_transform(
data[dataset][colname])
# Delete the sex column
data[dataset] = data[dataset][[
name for name in varnames if name != 'sex'
]]
cat_features_new = [
cat_features[i] for i in range(len(varnames))
if varnames[i] != 'sex'
]
if i == len(plot_datasets) - 1:
corr = compute_associations(data[dataset].astype(int),
nominal_columns=cat_features_new)
g1 = sns.heatmap(corr,
cmap="YlGnBu",
ax=axs[i],
cbar_ax=axs[-1])
else:
corr = compute_associations(data[dataset].astype(int),
nominal_columns=cat_features_new)
g1 = sns.heatmap(corr, cmap="YlGnBu", cbar=False, ax=axs[i])
g1.set_ylabel('')
g1.set_xlabel('')
g1.set_yticks([])
X_train=X_preprocessing.fit_transform(X_train)
X_cols=X_preprocessing.transformers_[0][1].named_steps["onehot"].get_feature_names(categorical_cols).tolist()+numeric_cols
X_train=pd.DataFrame(X_train, columns=X_cols)
X_test=X_preprocessing.fit_transform(X_test)
X_cols=X_preprocessing.transformers_[0][1].named_steps["onehot"].get_feature_names(categorical_cols).tolist()+numeric_cols
X_test=pd.DataFrame(X_test, columns=X_cols)
# endregion
# region Correlation & clustering
#The random forest works without problems with correlated features but in order to better asses the features importance
#it is importante to run a groupiwse permutation on the features, thus one has to create clusters of correlated features.
#The Dython library allows to compute association measures for a dataset made up by variables of diffent type.
#Pearson's r for quantitative variables pairs, correlation ratio for quantitative/nominal pairs and Cramers'V for nominals variables as default(or
# Theil's U)
dissimilarity=1-abs(compute_associations(X_train))
diss_condensed =squareform(dissimilarity)
diss_linkage = linkage(diss_condensed, method='complete')
fig, (ax1) = plt.subplots()
ax1.set(xlabel="Features", ylabel="Dissimilarity [0,1]")
plt.axhline(y=0.4, color='r', linestyle='--')
dendro = hierarchy.dendrogram(diss_linkage, labels=list(X_train.columns.values), ax=ax1,leaf_rotation=90)
plt.tight_layout()
#plt.savefig('Clusters.png', bbox_inches='tight')
#pickle.dump(fig,open("Clusters.pickle","wb"))
# endregion
# region Creation clusters
clusters=fcluster(diss_linkage,0.4,criterion='distance')
gow.append(np.mean(np.abs(true - imputed))/cont_range)
else:
gow.append((true.astype(int) != imputed.astype(int)).mean())
error.loc[method, 'gow'] = np.mean(gow)
#error.T[['full']].T.to_csv(res + 'Run' + str(run_idx) + '/res' + dataset_name + '.csv', index = False)
#=============================
# Comparing associations structure
#=============================
import seaborn as sns
from dython.nominal import compute_associations, associations
from sklearn.metrics.pairwise import cosine_similarity
original_assoc = compute_associations(full_pima, nominal_columns = cat_features)
associations(full_pima, nominal_columns = cat_features)
Ez = out2['Ez.y']
vc = vars_contributions(completed_y2, Ez, assoc_thr = 0.0, \
title = 'Contribution of the variables to the latent dimensions',\
storage_path = None)
assoc = cosine_similarity(vc, dense_output=True)
labels = ['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',
'BMI', 'D.P. Function', 'Age', 'Outcome']
fig, axn = plt.subplots(1, 2, sharex=True, sharey=True, figsize = (12,10))
cbar_ax = fig.add_axes([.91, .3, .03, .4])
def vars_contributions(df, latent_rpz, assoc_thr = 0.0, \
title = 'Contribution of the variables to the latent dimensions',\
storage_path = None):
'''
Plot the contribution of the original variables to the latent dimensions
constructed by the MDGMM
Parameters
----------
df : pandas DataFrame
The original variables.
latent_rpz : pandas DataFrame
The latent representation of the observations issued by the MDGMM.
assoc_thr : int, optional
The minimal association (in absolute value) with the latent
dimensions for a variable to be displayed.
The default is 0.0.
title : str, optional
The title of the plot to display. The default is 'Latent representation of the observations'.
storage : Bool
The path to store the plot
Returns
-------
corrs: The associations computed
'''
latent_dim = latent_rpz.shape[1]
if latent_dim > 2:
raise NotImplementedError('This function is intended for latent\
representation of dimension 2 for the moment'
)
if isinstance(latent_rpz, pd.DataFrame):
latent_rpz.columns = ['Latent dimension 1', 'Latent dimension 2']
else:
# Format the latent representation into a pandas DataFrame
latent_rpz = pd.DataFrame(
latent_rpz, columns=['Latent dimension 1', 'Latent dimension 2'])
# Latent representation of the variables
corrs = np.zeros((df.shape[1], latent_rpz.shape[1]))
for j1, original_col in enumerate(df.columns):
for j2, latent_col in enumerate(latent_rpz.columns):
old_new = pd.DataFrame(df[original_col]).join(
pd.DataFrame(latent_rpz[latent_col]))
assoc = compute_associations(old_new).iloc[1, 0]
corrs[j1, j2] = assoc
# Plot a variable factor map for the first two dimensions.
(fig, ax) = plt.subplots(figsize=(8, 8))
for i in range(df.shape[1]):
if (np.abs(corrs[i]) > assoc_thr).all():
ax.arrow(
0,
0, # Start the arrow at the origin
corrs[i, 0], #0 for PC1
corrs[i, 1], #1 for PC2
head_width=0.1,
head_length=0.1)
plt.text(corrs[i, 0] + 0.05,
corrs[i, 1] + 0.05,
s=df.columns.values[i])
an = np.linspace(0, 2 * np.pi, 300)
plt.plot(np.cos(an), np.sin(an)) # Add a unit circle for scale
plt.axis('equal')
plt.xlabel('Latent dimension 1', fontsize=16)
plt.ylabel('Latent dimension 2', fontsize=16)
ax.set_title(title)
if storage_path:
plt.savefig(storage_path)
plt.show()
return corrs
