#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)
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 = [
# -*- 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:"
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']
