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train_modified.py
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train_modified.py
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import data_io
import pickle
import feature_extractor as fe
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, GradientBoostingClassifier
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.pipeline import Pipeline
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import LabelBinarizer
from sklearn.grid_search import GridSearchCV
import pandas as pd
import csv
from time import time
import numpy
def get_pipeline():
features = fe.feature_extractor()
classifier = GradientBoostingClassifier(n_estimators=1024,
random_state = 1,
subsample = .8,
min_samples_split=10,
max_depth = 6,
verbose=3)
steps = [("extract_features", features),
("classify", classifier)]
myP = Pipeline(steps)
# params = {"classify__n_estimators": [768, 1024, 1536], "classify__min_samples_split": [1, 5, 10], "classify__min_samples_leaf": [1, 5, 10]}
# grid_search = GridSearchCV(myP, params, n_jobs=8)
# return grid_search
# return myP
return (features, classifier)
def get_types(data):
data['Bin-Bin'] = (data['A type']=='Binary')&(data['B type']=='Binary')
data['Num-Num'] = (data['A type']=='Numerical')&(data['B type']=='Numerical')
data['Cat-Cat'] = (data['A type']=='Categorical')&(data['B type']=='Categorical')
data[['A type','B type']] = data[['A type','B type']].replace('Binary',1)
data[['A type','B type']] = data[['A type','B type']].replace('Categorical',1)
data[['A type','B type']] = data[['A type','B type']].replace('Numerical',0)
return data
def combine_types(data, data_info):
data = pd.concat([data,data_info],axis = 1)
types = []
for a,b in zip(data['A type'], data['B type']):
types.append(a + b)
data['types'] = types
#data['types'] = [x + y for x in data['A type'] for y in data['B type']]
return data
"""
Return one classifier for one catagory
"""
def classify_catagory(train, test):
print("Train-test split")
trainX, testX, trainY, testY = train_test_split(train, test, random_state = 1)
print "TrainX size = ", str(trainX.shape)
print "TestX size = ", str(testX.shape)
classifier = GradientBoostingClassifier(n_estimators=1024,
random_state = 1,
subsample = .8,
min_samples_split=10,
max_depth = 6,
verbose=3)
classifier.fit(trainX, trainY)
print "Score = ", classifier.score(testX, testY)
feature_importrance = classifier.feature_importances_
logger = open(data_io.get_paths()["feature_importance_path"], "a")
for fi in feature_importrance:
logger.write(str(fi))
logger.write("\n")
logger.write("###########################################\n")
logger.close()
return classifier
"""
categories are: Num+Num, Num+~Num, ~NUm+Num, ~Num+~Num
"""
def create_classifiers(train, test, train_info):
num_num = (train_info["A type"] == "Numerical") & (train_info["B type"] == "Numerical")
num_num_classifier = classify_catagory(train[num_num], test[num_num])
num_not_num = (train_info["A type"] == "Numerical") & (train_info["B type"] != "Numerical")
num_not_num_classifier = classify_catagory(train[num_not_num], test[num_not_num])
not_num_num = (train_info["A type"] != "Numerical") & (train_info["B type"] == "Numerical")
not_num_num_classifier = classify_catagory(train[not_num_num], test[not_num_num])
not_num_not_num = (train_info["A type"] != "Numerical") & (train_info["B type"] != "Numerical")
not_num_not_num_classifier = classify_catagory(train[not_num_not_num], test[not_num_not_num])
return (num_num_classifier, num_not_num_classifier, not_num_num_classifier, not_num_not_num_classifier)
def main():
t1 = time()
print("Reading in the training data")
train = data_io.read_train_pairs()
train_info = data_io.read_train_info()
train = combine_types(train, train_info)
#make function later
train = get_types(train)
target = data_io.read_train_target()
print "Reading SUP data..."
for i in range(1,4):
print "SUP", str(i)
sup = data_io.read_sup_pairs(i)
sup_info = data_io.read_sup_info(i)
sup = combine_types(sup, sup_info)
sup = get_types(sup)
sup_target = data_io.read_sup_target(i)
train_info = train_info.append(sup_info)
train = train.append(sup)
target = target.append(sup_target)
# Old train
print "Reading old train data..."
old_train = data_io.read_old_train_pairs()
old_train_info = data_io.read_old_train_info()
old_train = combine_types(old_train, old_train_info)
old_train = get_types(old_train)
old_target = data_io.read_old_train_target()
train = train.append(old_train)
target = target.append(old_target)
# End old train
print "Train size = ", str(train.shape)
print("Extracting features and training model")
feature_trans = fe.feature_extractor()
orig_train = feature_trans.fit_transform(train)
orig_train = numpy.nan_to_num(orig_train)
classifier = classify_catagory(orig_train, target.Target)
#(both_classifier, A_classifier, B_classifier, none_classifier) = create_classifiers(orig_train, target.Target, train_info)
print("Saving features")
data_io.save_features(orig_train)
print("Saving the classifier")
#data_io.save_model( (both_classifier, A_classifier, B_classifier, none_classifier) )
data_io.save_model(classifier)
#features = [x[0] for x in classifier.steps[0][1].features ]
#csv_fea = csv.writer(open('features.csv','wb'))
#imp = sorted(zip(features, classifier.steps[1][1].feature_importances_), key=lambda tup: tup[1], reverse=True)
#for fea in imp:
# print fea[0], fea[1]
# csv_fea.writerow([fea[0],fea[1]])
t2 = time()
t_diff = t2 - t1
print "Time Taken (min):", round(t_diff/60,1)
if __name__ == "__main__":
main()