Plot confusion matrix and simplify plot selection

src/processing.py

@@ -7,7 +7,7 @@ from sklearn.neighbors import KNeighborsClassifier
 from sklearn.preprocessing import scale
 from sklearn.svm import LinearSVC
 from sklearn.tree import DecisionTreeClassifier
-from seaborn import set_theme
+from seaborn import set_theme, set_style, heatmap, FacetGrid
 from matplotlib.pyplot import *
 from pandas import DataFrame
 
@@ -53,41 +53,41 @@ def predict_data(data, target, model, results):
     return populated_results
 
 
-def plot_roc_auc_curve(model, results):
-    rounded_auc = round(results.loc[model]["auc"], 3)
-    plot(
-        results.loc[model]["fpr"],
-        results.loc[model]["tpr"],
-        label=f"{model} , AUC={rounded_auc}",
-    )
+def plot_roc_auc_curve(results):
+    fig = figure(figsize=(8, 6))
+    for model in results.index:
+        rounded_auc = round(results.loc[model]["auc"], 3)
+        plot(
+            results.loc[model]["fpr"],
+            results.loc[model]["tpr"],
+            label=f"{model} , AUC={rounded_auc}",
+        )
     xticks(arange(0.0, 1.0, step=0.1))
     yticks(arange(0.0, 1.0, step=0.1))
     legend(loc="lower right")
-
-
-def plot_confusion_matrix(model, results):
-    matrix = results.loc[model]["confusion_matrix"]
-    classes = ["Negative", "Positive"]
-    for item in matrix:
-        text(x=0.5, y=0.5, s=item)
-    xticks(ticks=arange(len(classes)), labels=classes)
-    yticks(ticks=arange(len(classes)), labels=classes)
-
-
-def choose_plot_type(type, model, results):
-    if type == "roc":
-        plot_roc_auc_curve(model, results)
-    elif type == "confusion_matrix":
-        plot_confusion_matrix(model, results)
-
-
-def plot_individual_figure(results, type, x_axis, y_axis, fig_title):
-    fig = figure(figsize=(8, 6))
-    for model in results.index:
-        choose_plot_type(type, model, results)
-    xlabel(x_axis)
-    ylabel(y_axis)
+    fig_title = "ROC AUC curve"
     title(fig_title)
+    xlabel("False positive rate")
+    ylabel("True positive rate")
+    fig.savefig(f"docs/assets/{fig_title.replace(' ', '_').lower()}.png")
+
+
+def plot_confusion_matrix(results):
+    set_style("white")
+    matrix = results.filter(items=["model", "confusion_matrix"])
+    fig, axes = subplots(nrows=1, ncols=5, figsize=(8, 6))
+    for i in range(len(axes)):
+        heatmap(
+            ax=axes[i],
+            data=matrix.iloc[i]["confusion_matrix"],
+            cmap="Blues",
+            square=True,
+            annot=True,
+            cbar=False,
+        )
+        axes[i].set_title(matrix.index[i])
+    fig_title = "Confusion Matrix"
+    suptitle(fig_title)
     show()
     fig.savefig(f"docs/assets/{fig_title.replace(' ', '_').lower()}.png")
 
@@ -95,20 +95,8 @@ def plot_individual_figure(results, type, x_axis, y_axis, fig_title):
 # TODO Add cross_val_score
 def plot_all_figures(results):
     set_theme()
-    plot_individual_figure(
-        results,
-        type="roc",
-        x_axis="False positive rate",
-        y_axis="True positive rate",
-        fig_title="ROC AUC curve",
-    )
-    plot_individual_figure(
-        results,
-        type="confusion_matrix",
-        x_axis="Predicted values",
-        y_axis="Real values",
-        fig_title="Confusion Matrix",
-    )
+    plot_roc_auc_curve(results=results)
+    plot_confusion_matrix(results=results)
 
 
 def create_result_dataframes():