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advcl_SVM_nonlinear.py
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advcl_SVM_nonlinear.py
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import matplotlib.pyplot as plt
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, StandardScaler
from sklearn.svm import SVC
import utils
def draw_confusion_matrix(Clf, X, y):
titles_options = [
("Confusion matrix, without normalization", None),
("NonLinear SVM Confusion matrix", "true"),
]
for title, normalize in titles_options:
disp = plot_confusion_matrix(Clf, X, y, cmap="Blues", normalize=normalize)
disp.ax_.set_title(title)
plt.show()
"""
# DATASET COMPLETO
df = utils.load_tracks(
"data/tracks.csv", dummies=True, buckets="continuous", fill=True, outliers=True
)
column2drop = [
("track", "language_code"),
]
df.drop(column2drop, axis=1, inplace=True)
print(df["album", "type"].unique())
# feature to reshape
label_encoders = dict()
column2encode = [
("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="continuous")
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
)
# STANDARDIZZO
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.fit_transform(X_test)
"""NON LINEAR SVM CLASSIFIER"""
"""valori con rbf test
Accuracy 0.9138018714401953
F1-score [0.95813611 0.2641196 0.65204003 0.49795918]
precision recall f1-score support
0 0.93 0.98 0.96 17183
1 0.77 0.16 0.26 998
2 0.65 0.65 0.65 1302
3 0.95 0.34 0.50 181
accuracy 0.91 19664
macro avg 0.83 0.53 0.59 19664
weighted avg 0.91 0.91 0.90 19664
valori con polynomial
Accuracy 0.9156834825061025
F1-score [0.95881468 0.29975826 0.66126543 0.5530303 ]
precision recall f1-score support
0 0.94 0.98 0.96 17183
1 0.77 0.19 0.30 998
2 0.66 0.66 0.66 1302
3 0.88 0.40 0.55 181
accuracy 0.92 19664
macro avg 0.81 0.56 0.62 19664
weighted avg 0.91 0.92 0.90 19664
valori con linear(stessa porcheria delle minear SVM)
si fa una prova con l'rbf e una con il polynomial per avere i valori besti
"""
clf = SVC(
gamma="auto",
kernel="poly",
)
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("NonLinear SVM 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()