File size: 5,666 Bytes
fdd9b49 | 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 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 | #The code is published under MIT license.
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn import cross_validation
from sklearn.cross_validation import StratifiedKFold
from sklearn.metrics import roc_curve, auc
from sklearn.linear_model import LogisticRegression
import pandas as pd
import csv
import numpy as np
def prepare_data(learn_file, labels_file):
"""
Open learning set, cluster labels
and change ZIP codes with corresponding
cluster label. Return X and y for learning.
"""
print "---preparing data...",
l_set = pd.read_csv(learn_file, sep='\t')
# t_set = pd.read_csv(test_file, sep='\t', header=None, names=['click', 'creative_id', 'zip', 'domain', 'page'])
# t_set = pd.read_csv(test_file, sep='\t', header=None, names=['creative_id', 'zip', 'domain', 'page'])
l_set = l_set.iloc[::5, :]
# t_set = t_set.iloc[::5, :]
#replace NaN values with zero.
l_set = l_set.fillna(0)
# t_set = t_set.fillna(0)
with open(labels_file, mode='r') as file_in:
reader = csv.reader(file_in)
c_labels = {float(rows[0]): rows[1] for rows in reader}
#change ZIP with label
l_set['zip'] = l_set['zip'].convert_objects(convert_numeric=True).dropna()
l_set['zip'] = l_set['zip'].map(c_labels.get)
# Change ZIP with label
# t_set['zip'] = t_set['zip'].convert_objects(convert_numeric=True).dropna()
# t_set['zip'] = t_set['zip'].map(c_labels.get)
l_set = l_set.reindex(np.random.permutation(l_set.index))
print "done---"
#remove where ZIP None - for testing on part data
# l_set = l_set[l_set.zip.notnull()]
# t_set = t_set[t_set.zip.notnull()]
#X for learning features, y for click
X = l_set[['creative_id', 'zip', 'domain']]
y = l_set['click']
# X_sub = t_set[['creative_id', 'zip', 'domain']]
# y_sub = t_set['click']
#Replace domain with numeric
unique_d = set(X['domain'])
# print len(unique_d)
# unique_d |= set(X_sub['domain'])
dict_d = {}
for c, d in enumerate(unique_d):
dict_d[d] = c
X['domain'] = X['domain'].map(dict_d.get)
X = X.fillna(0)
# X_sub['domain'] = X_sub['domain'].map(dict_d.get)
# X_sub = X_sub.fillna(0)
return X, y, # X_sub, y_sub
def random_forest(X, y, n_estimators):
"""
Scikit Random Forest implementation
with 100 trees, testing on 0.4 part
of the data, and train on 0.6.
"""
#Scale data
#X = StandardScaler().fit_transform(X)
#split data to train and test
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.4)
# print X_train
# print y_train
# create rfc object
forest = RandomForestClassifier(n_estimators=n_estimators)
#fit training data
prob = forest.fit(X_train, y_train, ).predict_proba(X_test)
#compute ROC
fpr, tpr, thresholds = roc_curve(y_test, prob[:, 1])
roc_auc = auc(fpr, tpr)
#print fpr, tpr, thresholds
print "AUC Random Forest: " + str(roc_auc)
def stacking_scikit(X, y, n_estimators):
"""
Stacking with classifiers from scikit-learn
library. Based on example
https://github.com/log0/vertebral/blob/master/stacked_generalization.py
"""
X = X.as_matrix()
y = y.as_matrix()
base_classifiers = [RandomForestClassifier(n_estimators=n_estimators),
ExtraTreesClassifier(n_estimators=n_estimators),
GradientBoostingClassifier(n_estimators=n_estimators)]
clf_names = ["Random Forest", "Extra Trees Classifier", "Gradient Boosting Classifier"]
# Divide data on training and test set
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.2)
# Arrays for classifier results
out_train = np.zeros((X_train.shape[0], len(base_classifiers)))
out_test = np.zeros((X_test.shape[0], len(base_classifiers)))
t_cv = list(StratifiedKFold(y_train, n_folds=5))
for i, clf in enumerate(base_classifiers):
print "Training classifier " + clf_names[i]
cv_probabilities = np.zeros((X_test.shape[0], len(t_cv)))
# cross validation train
for j, (train_i, test_i) in enumerate(t_cv):
X_train_0 = X_train[train_i]
y_train_0 = y_train[train_i]
X_test_0 = X_train[test_i]
# train each classifier
clf.fit(X_train_0, y_train_0)
# Get probabilities for click on internal test data
proba = clf.predict_proba(X_test_0)
out_train[test_i, i] = proba[:, 1]
# Probabilities for test data
proba_test = clf.predict_proba(X_test)
cv_probabilities[:, j] = proba_test[:, 1]
# Average of predictions
out_test[:, i] = cv_probabilities.mean(1)
print "Stacking with Logistic regression"
stack_clf = LogisticRegression(C=10)
stack_clf.fit(out_train, y_train)
stack_prediction = stack_clf.predict_proba(out_test)
#compute ROC
fpr, tpr, thresholds = roc_curve(y_test, stack_prediction[:, 1])
roc_auc = auc(fpr, tpr)
print "AUC Stacking: " + str(roc_auc)
#write to file
np.savetxt(fname="results.txt", X=stack_prediction[:, 1], fmt="%0.6f")
learning_set = "C:\BigData\Zemanta_challenge_1_data/training_set.tsv"
learning_part = "C:\BigData\Zemanta_challenge_1_data/training_part.tsv"
test_set = "C:\BigData\Zemanta_challenge_1_data/test_set.tsv"
labels = "hc_results.csv"
X, y = prepare_data(learning_set, labels)
random_forest(X, y, 10)
stacking_scikit(X, y, 10)
|