-
Notifications
You must be signed in to change notification settings - Fork 1
/
advcl_naivebayes_gaussian.py
158 lines (132 loc) · 4 KB
/
advcl_naivebayes_gaussian.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
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.naive_bayes import GaussianNB
from sklearn.preprocessing import LabelBinarizer, LabelEncoder
import utils
def draw_confusion_matrix(Clf, X, y):
titles_options = [
("Confusion matrix, without normalization", None),
("Gaussian Naive Bayes Confusion matrix", "true"),
]
for title, normalize in titles_options:
disp = plot_confusion_matrix(Clf, X, y, cmap="Purples", 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()
print("LISTA COLONNE")
column_names = list(df.columns)
for column_name in column_names:
unique_values = df[column_name].unique()
if len(unique_values) >= 10:
print(column_name, "more than 10 %s values" % df.dtypes[column_name], sep="\t")
else:
print(column_name, unique_values, sep="\t")
"""
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
)
"""NB GAUSSIAN"""
clf = GaussianNB()
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("Gaussian Naive Bayes 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()