-
Notifications
You must be signed in to change notification settings - Fork 0
/
classifiers.py
86 lines (71 loc) · 3.24 KB
/
classifiers.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
import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
from sklearn.model_selection import cross_val_score, cross_val_predict
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import confusion_matrix, classification_report
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
import catboost as cb
def _get_train_test_values():
df_train = pd.read_csv("train.csv")
df_test = pd.read_csv("test.csv")
X_train = df_train.drop("Target", axis=1)
y_train = df_train["Target"]
X_test = df_test.drop("Target", axis=1)
y_test = df_test["Target"]
scaler = MinMaxScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
return X_train, X_test, y_train, y_test
def knn_classifier():
BEST_K = 3 # Look the jupyter notebook for more explanations about the process
print("Running KNN Classifier...")
X_train, X_test, y_train, y_test = _get_train_test_values()
classifier = KNeighborsClassifier(n_neighbors=BEST_K)
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
cfm = confusion_matrix(y_test, y_pred)
print("the KNN cfm was: \n", cfm)
score = classifier.score(X_test, y_test)
print("the KNN score was:", score)
return (score, 'KNN')
def logistic_regression_classifier():
print("Running Logistic Regression Classifier...")
X_train, X_test, y_train, y_test = _get_train_test_values()
classifier = LogisticRegression(
penalty="l2", dual=True, max_iter=480, multi_class="auto", solver="liblinear", C=10)
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
cfm = confusion_matrix(y_test, y_pred)
print("the Logistic Regression cfm was: \n", cfm)
score = classifier.score(X_test, y_test)
print("the Logistic Regression score was:", score)
return (score, 'Logistic Regression')
def random_forest_classifier():
print("Running Random Forest Classifier...")
X_train, X_test, y_train, y_test = _get_train_test_values()
classifier = RandomForestClassifier(criterion='gini', max_depth=None, max_features='log2',
max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2, n_estimators=120)
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
cfm = confusion_matrix(y_test, y_pred)
print("the Random Forest cfm was: \n", cfm)
score = classifier.score(X_test, y_test)
print("the Random Forest score was:", score)
return (score, 'Random Forest')
def Catboost_classifier():
print("Running Catboost Classifier...")
X_train, X_test, y_train, y_test = _get_train_test_values()
classifier = cb.CatBoostClassifier(
depth=7, iterations=300, l2_leaf_reg=9, learning_rate=0.1)
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
cfm = confusion_matrix(y_test, y_pred)
print("the Catboost cfm was: \n", cfm)
score = classifier.score(X_test, y_test)
print("the Catboost score was:", score)
return (score, 'Catboost')