Ejemplos de ROC_Curve en Python

Ejemplo n.º 1

Archivo: Sklearn_LogisticRegression.py Proyecto: biprade/Applied_Machine_Learning

import pandas as pd

import ROC_Curve
from sklearn.cross_validation import train_test_split
# regr=linear_model.LogisticRegression()
data=pd.read_csv(open('/users/biprade/downloads/Project/train_Complete.csv'))
target=data['TripType']
del data['TripType']
X, y =data,target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.25, random_state=42)

a= ROC_Curve.getROCScore(X_train,y_train,X_test,y_test,'LogisticRegression',Cvalue=1000)
print a[0],a[1]

Ejemplo n.º 2

Archivo: Sklearn_DecisionTrees.py Proyecto: biprade/Applied_Machine_Learning

import pandas as pd

import ROC_Curve
from sklearn.cross_validation import train_test_split

data=pd.read_csv(open('train_Complete.csv'))
target=data['TripType']
del data['TripType']
X, y =data,target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.25, random_state=42)



a= ROC_Curve.getROCScore(X_train,y_train,X_test,y_test,'DecisionTree')
print a[0],a[1]

Ejemplo n.º 3

Archivo: Sklearn_Bagging.py Proyecto: biprade/Applied_Machine_Learning

import pandas as pd
from sklearn.metrics import accuracy_score
from sklearn import linear_model
from sklearn import tree
from sklearn.multiclass import OneVsRestClassifier
from sklearn.svm import LinearSVC
from sklearn.ensemble import BaggingClassifier
from sklearn.feature_selection import chi2
from sklearn.feature_selection import SelectKBest
from sklearn.cross_validation import train_test_split
import ROC_Curve

data = pd.read_csv(open("train_Complete.csv"))
target = data["TripType"]
del data["TripType"]
X, y = data, target
X_features = 150
ch2 = SelectKBest(chi2, k=X_features)
X = ch2.fit_transform(X, y)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)

a = ROC_Curve.getROCScore(X_train, y_train, X_test, y_test, "Bagging")
print a[0], a[1]

Ejemplo n.º 4

Archivo: Sklearn_SVM.py Proyecto: biprade/Applied_Machine_Learning

from sklearn.metrics import confusion_matrix
from sklearn.multiclass import OneVsRestClassifier
from sklearn.svm import LinearSVC
import pandas as pd
from sklearn.metrics import log_loss
import numpy as np
import ROC_Curve
from sklearn.externals import joblib
from sklearn.metrics import accuracy_score
from sklearn.cross_validation import train_test_split
from sklearn.feature_selection import chi2
from sklearn.feature_selection import SelectKBest
from sklearn.preprocessing import label_binarize
from sklearn.cross_validation import train_test_split
data=pd.read_csv(open('/users/biprade/downloads/Project/train_Complete.csv'))
target=data['TripType']
del data['TripType']
X, y =data,target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.25, random_state=42)


a= ROC_Curve.getROCScore(X_train,y_train,X_test,y_test,'LinearSVC',Cvalue=1)
print a[0],a[1]

Ejemplo n.º 5

Archivo: Sklearn_NaiveBayes.py Proyecto: biprade/Applied_Machine_Learning

import pandas as pd

import ROC_Curve
from sklearn.cross_validation import train_test_split
from sklearn.multiclass import OneVsRestClassifier
data=pd.read_csv(open('/users/biprade/downloads/Project/train_Complete.csv'))
target=data['TripType']
del data['TripType']
X, y =data,target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.25, random_state=42)

a= a= ROC_Curve.getROCScore(X_train,y_train,X_test,y_test,'NaiveBayes',alphaValue=0.6)
print a[0],a[1]