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(Refactored) Guarantee that labels are aligned #8

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(Refactored) Guarantee that labels are aligned #8

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35 changes: 22 additions & 13 deletions confusion_matrix_pretty_print.py
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Original file line number Diff line number Diff line change
Expand Up @@ -212,20 +212,29 @@ def plot_confusion_matrix_from_data(y_test, predictions, columns=None, annot=Tru
from sklearn.metrics import confusion_matrix
from pandas import DataFrame

#data
if(not columns):
#labels axis integer:
##columns = range(1, len(np.unique(y_test))+1)
#labels axis string:
from string import ascii_uppercase
columns = ['class %s' %(i) for i in list(ascii_uppercase)[0:len(np.unique(y_test))]]

confm = confusion_matrix(y_test, predictions)
cmap = 'Oranges';
fz = 11;
figsize=[9,9];
# data
if not columns:
generated_columns = [
"class {0}".format(str(x).upper()) for x in np.unique(y_test)
]

confm = confusion_matrix(y_test, predictions, labels=np.unique(y_test))
cmap = "Oranges"
fz = 11
figsize = [9, 9]
show_null_values = 2
df_cm = DataFrame(confm, index=columns, columns=columns)

df_cm = pd.DataFrame(
confm,
index=(np.unique(y_test) if not columns is None else generated_columns),
columns=(np.unique(y_test) if not columns is None else generated_columns),
)

# reorders confusion matrix to respect columns passed as arguments
if columns:
df_cm = df_cm.reindex(index=columns, columns=columns)
df_cm = df_cm.fillna(0.0)

pretty_plot_confusion_matrix(df_cm, fz=fz, cmap=cmap, figsize=figsize, show_null_values=show_null_values, pred_val_axis=pred_val_axis)
#

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