From 0f515314b9a3aef991daff97b318d07587ebe07d Mon Sep 17 00:00:00 2001
From: ugmom <ugmom@student.kit.edu>
Date: Sun, 23 Mar 2025 20:28:02 +0100
Subject: [PATCH] fixed count plot
---
code/machine_learning_models/decision_tree.py | 9 ++-------
code/machine_learning_models/knn.py | 6 ++----
.../machine_learning_models/logistic_regression.py | 5 +++--
code/machine_learning_models/random_forest.py | 8 +++++++-
code/machine_learning_models/utilities.py | 14 ++++++++------
5 files changed, 22 insertions(+), 20 deletions(-)
diff --git a/code/machine_learning_models/decision_tree.py b/code/machine_learning_models/decision_tree.py
index c46df4d..2438a15 100644
--- a/code/machine_learning_models/decision_tree.py
+++ b/code/machine_learning_models/decision_tree.py
@@ -9,8 +9,7 @@ from sklearn.metrics import classification_report, confusion_matrix
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.tree import DecisionTreeClassifier
-from utilities import plot_counts
-from utilities import plot_features, ordinal_encode, normalize, plot_confusion_matrix, print_high_confidence_samples, import_data
+from utilities import plot_features, ordinal_encode, normalize, plot_confusion_matrix, print_high_confidence_samples, import_data, plot_counts
warnings.filterwarnings("ignore")
@@ -77,14 +76,9 @@ def train():
print("Training complete.")
def graphs():
-
y_prediction = dtc.predict(X_test)
print("Classification report: \n", classification_report(y_test, y_prediction))
- # Plot absolute quantities of class 0 and class 1
- sns.countplot(x=y_data, data=df_train)
- plot_counts(model_name=model_name)
-
# Determine feature importance
features = pd.DataFrame(dtc.feature_importances_,
index=X_train.columns,
@@ -96,5 +90,6 @@ def graphs():
model_name=model_name)
print_high_confidence_samples(model=dtc, x=X_train)
+ plot_counts(y_data, df_train)
print("Graphs complete.")
\ No newline at end of file
diff --git a/code/machine_learning_models/knn.py b/code/machine_learning_models/knn.py
index cd62cd8..37d9795 100644
--- a/code/machine_learning_models/knn.py
+++ b/code/machine_learning_models/knn.py
@@ -8,7 +8,7 @@ from sklearn.metrics import classification_report, confusion_matrix
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler, LabelEncoder
-from utilities import ordinal_encode, normalize, plot_confusion_matrix, plot_counts, import_data, plot_roc_curve
+from utilities import ordinal_encode, normalize, plot_confusion_matrix, import_data, plot_roc_curve, plot_counts
warnings.filterwarnings("ignore")
@@ -58,8 +58,6 @@ def graphs():
# Calculate prediction probabilities for ROC curve
y_score = model.predict_proba(X_test)[:, 1]
plot_roc_curve(y_test, y_score, model_name=model_name)
+ plot_counts(y_data, df_train)
- # Plot absolute quantities of class 0 and class 1
- sns.countplot(x=y_data, data=df_train)
- plot_counts(model_name=model_name)
print("Graphs complete.")
\ No newline at end of file
diff --git a/code/machine_learning_models/logistic_regression.py b/code/machine_learning_models/logistic_regression.py
index a0aea01..1b56321 100644
--- a/code/machine_learning_models/logistic_regression.py
+++ b/code/machine_learning_models/logistic_regression.py
@@ -1,6 +1,5 @@
import numpy as np
import pandas as pd
-import seaborn as sns
import os
import warnings
@@ -9,7 +8,7 @@ from sklearn.model_selection import learning_curve
from sklearn.preprocessing import StandardScaler, LabelEncoder
from utilities import ordinal_encode, heat_map, plot_features, plot_confusion_matrix, normalize, \
- print_high_confidence_samples, plot_counts, import_data, plot_roc_curve, plot_learning_curve
+ print_high_confidence_samples, import_data, plot_roc_curve, plot_learning_curve, plot_counts
warnings.filterwarnings("ignore")
@@ -117,4 +116,6 @@ def graphs():
# Plot ROC curve using the function from utilities
plot_roc_curve(y_test, y_score, model_name=model_name)
+ plot_counts(y_data, df_train)
+
print("Graphs complete.")
diff --git a/code/machine_learning_models/random_forest.py b/code/machine_learning_models/random_forest.py
index 6e4168d..7fa6abb 100644
--- a/code/machine_learning_models/random_forest.py
+++ b/code/machine_learning_models/random_forest.py
@@ -10,7 +10,7 @@ from sklearn.model_selection import learning_curve
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, confusion_matrix, precision_recall_curve
-from utilities import plot_precision_recall_curve, plot_learning_curve, import_data, plot_roc_curve
+from utilities import plot_precision_recall_curve, plot_learning_curve, import_data, plot_roc_curve, plot_counts
from utilities import ordinal_encode, heat_map, plot_features, plot_confusion_matrix, normalize, print_high_confidence_samples
sys.stdout.reconfigure(line_buffering=True)
@@ -95,4 +95,10 @@ def graphs():
# Plot ROC curve using the function from utilities
plot_roc_curve(y_test, y_score, model_name=model_name)
+ plot_counts(y_data, df_train)
+
print("Graphs complete.")
+
+if __name__ == "__main__":
+ train()
+ graphs()
\ No newline at end of file
diff --git a/code/machine_learning_models/utilities.py b/code/machine_learning_models/utilities.py
index 54a77c3..17268dc 100644
--- a/code/machine_learning_models/utilities.py
+++ b/code/machine_learning_models/utilities.py
@@ -11,6 +11,7 @@ from sklearn.preprocessing import OrdinalEncoder
show_plots = False
+y_data = ['normal', 'anomaly']
# Plots
def heat_map(df, model_name=None):
@@ -34,12 +35,6 @@ def heat_map(df, model_name=None):
if show_plots:
plt.show()
-def plot_counts(model_name=None):
- if model_name:
- save_plot(model_name + " - Counts")
- if show_plots:
- plt.show()
-
def plot_xy(df, x, y, model_name=None):
"""
Creates a scatter plot for two numerical columns.
@@ -153,6 +148,13 @@ def plot_learning_curve(train_sizes, train_scores, test_scores, model_name=None)
if show_plots:
plt.show()
+def plot_counts(target, df):
+ plt.clf()
+ sns.countplot(x = target, data = df)
+ save_plot("Count")
+ if show_plots:
+ plt.show()
+
def plot_roc_curve(y_true, y_score, model_name=None):
"""
Plots the ROC curve for a binary classification model.
--
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