Пример #1
0
#from sklearn.svm import SVC
#from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.metrics import accuracy_score
from sklearn import cross_validation
from sklearn.pipeline import Pipeline
from temporalPivot import playByPlay
from sklearn.preprocessing import MinMaxScaler

from sklearn import linear_model

pbp = playByPlay()
'''
pbp.select("KC",2010)
preppedData = pbp.temporal(20)

logistic = linear_model.LogisticRegression(C=100, solver='newton-cg')

pipeline = Pipeline([('min/max scaler',MinMaxScaler(feature_range=(0.0, 1.0))),
                     ('logistic',logistic)])
print "Classifier created"

print "Train Classification report:"
pipeline.fit(preppedData['train'],preppedData['label'])

y_true, y_pred = preppedData['label'], pipeline.predict(preppedData['train'])
print classification_report(y_true,y_pred)
print accuracy_score(y_true,y_pred)

print "CrossValidation:"
scores = cross_validation.cross_val_score(pipeline,preppedData['train'],preppedData['label'],cv=10)
Пример #2
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import pickle
import math
from sknn.mlp import Classifier, Layer
from sknn import ae
from sklearn.pipeline import Pipeline
from temporalPivot import playByPlay
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import accuracy_score
from sklearn.grid_search import GridSearchCV
from sklearn.metrics import classification_report

pbp = playByPlay()  #instantiate object
pbp.select("CAR", 2014)  #select team and year, this is done in place
preppedData = pbp.temporal(20)  #this will return the training and test data
#as dict preppedData['train'], preppedData['label']


def neuralCombo(data):
    pipeline = Pipeline([('min/max scaler',
                          MinMaxScaler(feature_range=(0.0, 1.0))),
                         ('nn',
                          Classifier(layers=[
                              Layer("Rectifier", units=100),
                              Layer("Sigmoid", units=100),
                              Layer("Softmax")
                          ],
                                     n_iter=25))])

    learningRate = [0.05, 0.005, 0.001, 0.0001, 0.00001]
    units = [5, 50, 100, 200]
    type = [
Пример #3
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# -*- coding: utf-8 -*-
"""
Created on Sat Dec 12 11:49:55 2015

@author: Adithya
"""
from sklearn.svm import SVC
from sklearn.metrics import classification_report
from sklearn.metrics import accuracy_score
from sklearn import cross_validation
from sklearn.pipeline import Pipeline
from temporalPivot import playByPlay
from sklearn.preprocessing import MinMaxScaler

pbp = playByPlay()
pbp.select("CAR",2014)
preppedData = pbp.temporal(20)


pipeline = Pipeline([('min/max scaler',MinMaxScaler(feature_range=(0.0, 1.0))),
                     ('svm',SVC(kernel='poly',C=100,degree=5))])
print "Classifier created"

print "Train Classification report:"
pipeline.fit(preppedData['train'],preppedData['label'])

y_true, y_pred = preppedData['label'], pipeline.predict(preppedData['train'])
print classification_report(y_true,y_pred)
print accuracy_score(y_true,y_pred)

print "CrossValidation:"
Пример #4
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import pickle
import math
from sknn.mlp import Classifier, Layer
from sknn import ae
from sklearn.pipeline import Pipeline
from temporalPivot import playByPlay
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import accuracy_score
from sklearn.grid_search import GridSearchCV
from sklearn.metrics import classification_report



pbp = playByPlay()	#instantiate object
pbp.select("CAR", 2014)	#select team and year, this is done in place 
preppedData = pbp.temporal(20)		#this will return the training and test data
									#as dict preppedData['train'], preppedData['label']

def neuralCombo(data):
	pipeline = Pipeline([
        ('min/max scaler', MinMaxScaler(feature_range=(0.0, 1.0))),
        ('nn', Classifier(layers=[
        	Layer("Rectifier", units=100),
        	Layer("Sigmoid", units=100),
        	Layer("Softmax")], 
        	n_iter=25))])

	learningRate = [0.05, 0.005, 0.001, 0.0001, 0.00001]
	units = [5, 50, 100, 200]
	type = ['Rectifier', 'Sigmoid', 'Sigmoid', 'Tanh', 'Linear', 'Softmax', 'Gaussian']
	#type = ['Rectifier', 'Linear', 'Gaussian']