advcl_SVM_linear.py

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 LabelEncoder
from sklearn.svm import LinearSVC

import utils


def draw_confusion_matrix(Clf, X, y):
    titles_options = [
        ("Confusion matrix, without normalization", None),
        ("Linear 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)


# 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="discrete")
# CAMBIO ALBUM TYPE IN BINARIA
print("prima", df["album", "type"].unique())
df["album", "type"] = df["album", "type"].replace(
    ["Single Tracks", "Live Performance", "Radio Program"],
    ["NotAlbum", "NotAlbum", "NotAlbum"],
)
print("dopo", df["album", "type"].unique())


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
)

"""LINEAR SVM """
# Best: {'random_state': 42, 'max_iter': 25000, 'loss': 'squared_hinge', 'C': 100}
clf = LinearSVC(
    C=100,  # best param 0.01
    random_state=40,
    max_iter=25000,  # 3000 per completo
    loss="squared_hinge",
)
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"""
from sklearn.metrics import auc, roc_auc_score, roc_curve

fpr, tpr, _ = roc_curve(y_test, y_pred)
roc_auc = auc(fpr, tpr)
print(roc_auc)

roc_auc = roc_auc_score(y_test, y_pred, average=None)

plt.figure(figsize=(8, 5))
plt.plot(fpr, tpr, label="ROC curve (area = %0.2f)" % (roc_auc))

plt.plot([0, 1], [0, 1], "k--")
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.title("Linear 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()

"""
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(1):
    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(1):
    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("Linear 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()
"""

"""RANDOM SEARCH PIU' VELOCE

print("STA FACENDO LA GRIDSEARCH")
param_list = {
    "loss": ["hinge", "squared_hinge"],
    "C": [0.1, 0.01, 1, 10, 100],
    "random_state": [10, 20, 30, 40, 50],
    "max_iter": [1000, 2000, 3000, 4000, 5000],
}

random_search = RandomizedSearchCV(clf, param_distributions=param_list, n_iter=20, cv=5)
random_search.fit(X_train, y_train)
clf = random_search.best_estimator_

y_pred = clf.predict(X_test)
# Print The value of best Hyperparameters
print(
    "Best:",
    random_search.cv_results_["params"][
        random_search.cv_results_["rank_test_score"][0]
    ],
)
"""