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model_pred.py
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from sklearn import preprocessing
import pandas as pd
import numpy as np
from collections import Counter
import matplotlib.pyplot as plt
import cPickle as pickle
from sklearn.naive_bayes import MultinomialNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression as LR
from sklearn.ensemble import RandomForestClassifier as RF
from sklearn import svm
from sklearn.cross_validation import KFold, train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import auc, f1_score, accuracy_score, precision_score, recall_score, roc_curve, roc_auc_score, confusion_matrix
def get_feature_matrix_labels(df_main, df_att, LTV_split):
df_main['first_purch_date'] = pd.to_datetime(df_main['first_purch_date'])
df_main['first_use_date'] = pd.to_datetime(df_main['first_use_date'])
df_main['first_use_to_first_purch'] = df_main['first_purch_date'] - df_main['first_use_date']
df_main = df_main.iloc[np.where(df_main['first_use_to_first_purch']>0.)[0]]
df_main['first_use_to_first_purch'] = df_main['first_use_to_first_purch'].apply(lambda x: x.item()/float(8.64*10**13))
df = pd.merge(df_main, df_att[['user_id','user_source']], on = 'user_id', how = 'left')
store_dummies = pd.core.reshape.get_dummies(df['most_used_store'])
source_dummies = pd.core.reshape.get_dummies(df['user_source'])
X = df[['use_count','first_use_to_first_purch','mean_freq','std_freq','num_items_purch','first_purchase_amount']]
#X = df[['first_use_to_first_purch','mean_freq','std_freq','first_purchase_amount']]
#X = pd.concat([X, source, store_dummies], axis = 1)
labels = np.where(df.LTV<LTV_split,1,0)
return X, labels
def run_model(Model, X_train, X_test, y_train, y_test):
if Model == LR:
m = Model(C=0.01)
elif Model == svm:
m = Model.SVC(kernel='rbf', probability=True)
else:
m = Model()
fit_ = m.fit(X_train, y_train)
y_predict = m.predict(X_test)
probas_ = fit_.predict_proba(X_test)
fpr, tpr, thresholds = roc_curve(y_test, probas_[:,1])
return accuracy_score(y_test, y_predict), \
f1_score(y_test, y_predict), \
precision_score(y_test, y_predict), \
recall_score(y_test, y_predict), \
roc_auc_score(y_test, probas_[:,1]), fpr, tpr, confusion_matrix(y_test, y_predict)
# auc(fpr,tpr), fpr, tpr, confusion_matrix(y_test, y_predict)
def get_training_test_indices(labels, num_folds):
kf = KFold(labels.shape[0], n_folds=num_folds)
return kf
def get_scores(X, labels, models, kf):
scores_ = []
for name, Model in models.iteritems():
accs_ = []
f1s_ = []
precs_ = []
recs_ = []
roc_aucs_ = []
for train_index, test_index in kf:
X_train, X_test = X.iloc[train_index], X.iloc[test_index]
y_train, y_test = labels[train_index], labels[test_index]
acc, f1, prec, rec, roc_auc, fpr, tpr, confusion_matrix_ = run_model(Model, X_train, X_test, y_train, y_test)
# print "%.4f\t%.4f\t%.4f\t%.4f\t%.4f\t%s" % (acc, f1, prec, rec, roc_auc, name)
accs_.append(acc)
f1s_.append(f1)
precs_.append(prec)
recs_.append(rec)
roc_aucs_.append(roc_auc)
roc_aucs_.append(roc_auc)
# print "%.4f\t%.4f\t%.4f\t%.4f\t%.4f\t%s" % (np.mean(accs_), np.mean(f1s_), np.mean(precs_), np.mean(recs_), np.mean(roc_aucs_), name)
scores_.append([name, np.mean(accs_), np.mean(f1s_), np.mean(precs_), np.mean(recs_), np.mean(roc_aucs_)])
return scores_
def get_fprs_tprs(X, labels, models):
# this function is mainly used to get fprs and tprs to be used for later plotting
X_train, X_test, y_train, y_test = train_test_split(X, labels, test_size=0.33, random_state=42)
fprs_ = []
tprs_ = []
for name, Model in models.iteritems():
acc, f1, prec, rec, roc_auc, fpr, tpr, confusion_matrix_ = run_model(Model, X_train, X_test, y_train, y_test)
fprs_.append(fpr)
tprs_.append(tpr)
return fprs_, tprs_
def main(inputfile_feat, inputfile_attribute, outputfile, LTV_split = 200):
df_main = pd.read_csv(inputfile_feat)
df_att = pd.read_csv(inputfile_attribute)
X, labels = get_feature_matrix_labels(df_main, df_att, LTV_split)
print "Split of labels: ", Counter(labels)
models = {"Logistic Regression": LR, \
"kNN": KNeighborsClassifier, \
"Naive Bayes": MultinomialNB, \
"Random Forest": RF }#, \
# "SVM": svm}
num_folds = 3
kf = get_training_test_indices(labels, num_folds)
scores_ = get_scores(X, labels, models, kf)
fprs_, tprs_ = get_fprs_tprs(X, labels, models)
pickle.dump((scores_, fprs_, tprs_), open(outputfile, 'wb'))
if __name__ == '__main__':
main(inputfile_feat, inputfile_attribute, outputfile)