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advcl_EnsClass_Boosting.py
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advcl_EnsClass_Boosting.py
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import matplotlib.pyplot as plt
from sklearn.ensemble import AdaBoostClassifier
from sklearn.metrics import (
accuracy_score,
auc,
classification_report,
f1_score,
plot_confusion_matrix,
roc_auc_score,
roc_curve,
)
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer, LabelEncoder
from sklearn.tree import DecisionTreeClassifier
import utils
def draw_confusion_matrix(Clf, X, y):
titles_options = [
("Confusion matrix, without normalization", None),
("Boosting Confusion matrix", "true"),
]
for title, normalize in titles_options:
disp = plot_confusion_matrix(Clf, X, y, cmap="Greens", normalize=normalize)
disp.ax_.set_title(title)
plt.show()
"""
# DATASET
df = utils.load_tracks(
"data/tracks.csv", dummies=True, buckets="discrete", fill=True, outliers=True
)
print(df["album", "type"].unique())
# feature to reshape
label_encoders = dict()
column2encode = [
("track", "language_code"),
("album", "listens"),
("album", "type"),
("track", "license"),
("album", "comments"),
("album", "date_created"),
("album", "favorites"),
("artist", "comments"),
("artist", "date_created"),
("artist", "favorites"),
("track", "comments"),
("track", "date_created"),
("track", "duration"),
("track", "favorites"),
("track", "interest"),
("track", "listens"),
]
for col in column2encode:
le = LabelEncoder()
df[col] = le.fit_transform(df[col])
label_encoders[col] = le
df.info()
"""
# DATASET PICCOLINO
df = utils.load_small_tracks(buckets="discrete")
label_encoders = dict()
column2encode = [
("track", "duration"),
("track", "interest"),
("track", "listens"),
("album", "type"),
]
for col in column2encode:
le = LabelEncoder()
df[col] = le.fit_transform(df[col])
label_encoders[col] = le
df.info()
class_name = ("album", "type")
attributes = [col for col in df.columns if col != class_name]
X = df[attributes].values
y = df[class_name]
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=100, stratify=y
)
"""ADA BOOST DECISION TREE"""
clf = AdaBoostClassifier(
base_estimator=DecisionTreeClassifier(
criterion="gini",
max_depth=9,
min_samples_split=10,
min_samples_leaf=10,
),
n_estimators=100,
random_state=0,
)
clf.fit(X_train, y_train)
# Apply on the training set
print("Apply on the training set: \n")
Y_pred = clf.predict(X_train)
print("Accuracy %s" % accuracy_score(y_train, Y_pred))
print("F1-score %s" % f1_score(y_train, Y_pred, average=None))
print(classification_report(y_train, Y_pred))
# Apply on the test set and evaluate the performance
print("Apply on the test set and evaluate the performance: \n")
y_pred = clf.predict(X_test)
print("Accuracy %s" % accuracy_score(y_test, y_pred))
print("F1-score %s" % f1_score(y_test, y_pred, average=None))
print(classification_report(y_test, y_pred))
draw_confusion_matrix(clf, X_test, y_test)
# ROC CURVE
lb = LabelBinarizer()
lb.fit(y_test)
lb.classes_.tolist()
fpr = dict()
tpr = dict()
roc_auc = dict()
by_test = lb.transform(y_test)
by_pred = lb.transform(y_pred)
for i in range(4):
fpr[i], tpr[i], _ = roc_curve(by_test[:, i], by_pred[:, i])
roc_auc[i] = auc(fpr[i], tpr[i])
roc_auc = roc_auc_score(by_test, by_pred, average=None)
plt.figure(figsize=(8, 5))
for i in range(4):
plt.plot(
fpr[i],
tpr[i],
label="%s ROC curve (area = %0.2f)" % (lb.classes_.tolist()[i], roc_auc[i]),
)
plt.plot([0, 1], [0, 1], "k--")
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.title("Boosting Decision Tree Roc-Curve")
plt.xlabel("False Positive Rate", fontsize=10)
plt.ylabel("True Positive Rate", fontsize=10)
plt.tick_params(axis="both", which="major", labelsize=12)
plt.legend(loc="lower right", fontsize=7, frameon=False)
plt.show()
"""BOOSTING RANDOM FOREST
Risultati, ci mette anche lei mezz'ora
Accuracy 0.9798616761594793
F1-score [0.98922749 0.87834821 0.94351631 0.80921053]
precision recall f1-score support
0 0.98 1.00 0.99 17183
1 0.99 0.79 0.88 998
2 0.98 0.91 0.94 1302
3 1.00 0.68 0.81 181
accuracy 0.98 19664
macro avg 0.99 0.84 0.91 19664
weighted avg 0.98 0.98 0.98 19664
clf = AdaBoostClassifier(
base_estimator=RandomForestClassifier(
n_estimators=100,
criterion="gini",
max_depth=17,
min_samples_split=3,
min_samples_leaf=3,
max_features="auto",
random_state=10,
class_weight="balanced",
),
n_estimators=100,
random_state=0,
)
clf.fit(X_train, y_train)
# Apply on the training set
print("Apply on the training set: \n")
Y_pred = clf.predict(X_train)
print("Accuracy %s" % accuracy_score(y_train, Y_pred))
print("F1-score %s" % f1_score(y_train, Y_pred, average=None))
print(classification_report(y_train, Y_pred))
# Apply on the test set and evaluate the performance
print("Apply on the test set and evaluate the performance: \n")
y_pred = clf.predict(X_test)
print("Accuracy %s" % accuracy_score(y_test, y_pred))
print("F1-score %s" % f1_score(y_test, y_pred, average=None))
print(classification_report(y_test, y_pred))
draw_confusion_matrix(clf, X_test, y_test)
"""
"""ROC CURVE"""
lb = LabelBinarizer()
lb.fit(y_test)
lb.classes_.tolist()
fpr = dict()
tpr = dict()
roc_auc = dict()
by_test = lb.transform(y_test)
by_pred = lb.transform(y_pred)
for i in range(4):
fpr[i], tpr[i], _ = roc_curve(by_test[:, i], by_pred[:, i])
roc_auc[i] = auc(fpr[i], tpr[i])
roc_auc = roc_auc_score(by_test, by_pred, average=None)
plt.figure(figsize=(8, 5))
for i in range(4):
plt.plot(
fpr[i],
tpr[i],
label="%s ROC curve (area = %0.2f)" % (lb.classes_.tolist()[i], roc_auc[i]),
)
plt.plot([0, 1], [0, 1], "k--")
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.title("Boosting Random Forest Roc-Curve")
plt.xlabel("False Positive Rate", fontsize=10)
plt.ylabel("True Positive Rate", fontsize=10)
plt.tick_params(axis="both", which="major", labelsize=12)
plt.legend(loc="lower right", fontsize=7, frameon=False)
plt.show()