def decision_tree(load_model=False):
start = time.time()
if load_model == False:
printYellow("* Decision tree model training started...")
# Create training set of 100,000 samples
n_max = 100000
X_dict_train, y_train = process_data(100000)
# Transform training dictionary into one-hot encoded vectors
dict_one_hot_encoder = DictVectorizer(sparse=False)
X_train = dict_one_hot_encoder.fit_transform(X_dict_train)
# print(len(X_train[0]))
# Creating test set and turn into one-hot encoded vectors
X_dict_test, y_test = process_data(100000, 100000)
X_test = dict_one_hot_encoder.transform(X_dict_test)
# print(len(X_test[0]))
# Load Model
if load_model == True:
printGreen('✔ Loading model from previous training...')
d_tree_file = open('../models/decision_tree_model.sav', 'rb')
decision_tree_final = pickle.load(d_tree_file)
# d_tree_file.close()
# Evaluate model on test set
prob = decision_tree_final.predict_proba(X_test)[:, 1]
score = roc_auc_score(y_test, prob)
printGreen('✔ ROC AUC score on test set: {0:.3f}'.format(score))
d_tree_file.close()
return 0
# Train decision tree classifier
params = {'max_depth': [3, 10, None]}
decision_tree_model = DecisionTreeClassifier(criterion='gini',
min_samples_split=30)
grid_search = GridSearchCV(decision_tree_model, params, n_jobs=-1, cv=3, scoring='roc_auc')
# print("Training started..")
grid_search.fit(X_train, y_train)
printGreen('✔ Decision tree model training complete..."\t\t{0:.1f}s'.format(time.time() - start))
# Use model with best parameter as final model
decision_tree_final = grid_search.best_estimator_
# Evaluate and run model on training data
prob = decision_tree_final.predict_proba(X_test)[:, 1]
score = roc_auc_score(y_test, prob)
printGreen('✔ ROC AUC score on test set: {0:.3f}'.format(score))
# Save Model
decision_tree_model_file = open('../models/decision_tree_model.sav', "wb")
pickle.dump(decision_tree_final, decision_tree_model_file)
decision_tree_model_file.close()
printGreen('✔ Decision tree model saved...')
return 0
def logistic_regression_ol(load_model=False):
start = time.time()
if load_model == False:
printYellow("* Logistic regression (using online learning) model training started...")
# Build Classifier
sgd_log_reg_model = SGDClassifier(loss='log', penalty=None, fit_intercept=True, n_iter=1, learning_rate='constant', eta0=0.01)
# Training sets
X_dict_train, y_train = process_data(100000)
dict_one_hot_encoder = DictVectorizer(sparse=False)
X_train = dict_one_hot_encoder.fit_transform(X_dict_train)
X_train_100k = X_train
y_train_100k = np.array(y_train)
# Test sets
X_dict_test, y_test_next10k = process_data(10000, 100000)
X_test_next10k = dict_one_hot_encoder.transform(X_dict_test)
if load_model == True:
printGreen('✔ Loading model from previous training...')
l_reg_file = open('../models/logistic_regression_model_ol.sav', 'rb')
sgd_log_reg_model = pickle.load(l_reg_file)
X_dict_test, y_test_next = process_data(10000, (20 + 1) * 200000)
X_test_next = dict_one_hot_encoder.transform(X_dict_test)
predict = sgd_log_reg_model.predict_proba(X_test_next)[:, 1]
score = roc_auc_score(y_test_next, predict)
printGreen("✔ ROC AUC score on test set: {0:.3f}".format(score))
return 0
# Train and partially fit on 1 million samples
for i in range(20):
X_dict_train, y_train_every = process_data(100000, i * 100000)
X_train_every = dict_one_hot_encoder.transform(X_dict_train)
sgd_log_reg_model.partial_fit(X_train_every, y_train_every, classes=[0, 1])
printGreen('✔ Logistic regression (using online learning) model training complete..."\t\t{0:.1f}s'.format(time.time() - start))
# Get test set
X_dict_test, y_test_next = process_data(10000, (i + 1) * 200000)
X_test_next = dict_one_hot_encoder.transform(X_dict_test)
# Evaluate
predict = sgd_log_reg_model.predict_proba(X_test_next)[:, 1]
score = roc_auc_score(y_test_next, predict)
printGreen("✔ ROC AUC score on test set: {0:.3f}".format(score))
# Save Model
l_reg_file = open('../models/logistic_regression_model_ol.sav', "wb")
pickle.dump(sgd_log_reg_model, l_reg_file)
l_reg_file.close()
printGreen('✔ Logistic regression (using online learning) model saved...')
return 0
def random_forest(load_model=False):
start = time.time()
if load_model == False:
printYellow("* Random forest model training started...")
# Create training set of 100,000 samples
n_max = 100000
X_dict_train, y_train = process_data(100000)
# Transform training dictionary into one-hot encoded vectors
dict_one_hot_encoder = DictVectorizer(sparse=False)
X_train = dict_one_hot_encoder.fit_transform(X_dict_train)
# Creating test set and turn into one-hot encoded vectors
X_dict_test, y_test = process_data(100000, 100000)
X_test = dict_one_hot_encoder.transform(X_dict_test)
# Load model instead of training again..
if load_model == True:
printGreen('✔ Loading model from previous training...')
r_forest_file = open('../models/random_forest_model.sav', 'rb')
random_forest_final = pickle.load(r_forest_file)
probs = random_forest_final.predict_proba(X_test)[:, 1]
score = roc_auc_score(y_test, probs)
printGreen('✔ ROC AUC score on test set: {0:.3f}'.format(score))
r_forest_file.close()
return 0
# Train random forest classifier
params = {'max_depth': [3, 10, None]}
random_forest_model = RandomForestClassifier(n_estimators=100, criterion='gini', min_samples_split=30,
n_jobs=-1)
grid_search = GridSearchCV(random_forest_model, params, n_jobs=-1, cv=3, scoring='roc_auc')
grid_search.fit(X_train, y_train)
printGreen('✔ Random forest model training complete..."\t\t{0:.1f}s'.format(time.time() - start))
# Use best paramter for final model
random_forest_final = grid_search.best_estimator_
# Evaluate model
probs = random_forest_final.predict_proba(X_test)[:, 1]
score = roc_auc_score(y_test, probs)
printGreen('✔ ROC AUC score on test set: {0:.3f}'.format(score))
# Save Model
random_forest_file = open('../models/random_forest_model.sav', "wb")
pickle.dump(random_forest_final, random_forest_file)
random_forest_file.close()
printGreen('✔ Random forest model saved...')
return 0
def logistic_regression(sample_size=100000, load_model=False):
start = time.time()
if load_model == False:
printYellow("* Logistic regression model training started...")
# Create Training Set
n = sample_size
X_dict_train, y_train = process_data(n)
dict_one_hot_encoder = DictVectorizer(sparse=False)
X_train = dict_one_hot_encoder.fit_transform(X_dict_train)
# Create Test Set
X_dict_test, y_test = process_data(n, n)
X_test = dict_one_hot_encoder.transform(X_dict_test)
X_train_n = X_train
y_train_n = np.array(y_train)
# Load model instead of training again
if load_model == True:
printGreen('✔ Loading model from previous training...')
l_reg_file = open('../models/logistic_regression_model.sav', 'rb')
sgd_log_reg_model = pickle.load(l_reg_file)
predictions = sgd_log_reg_model.predict_proba(X_test)[:, 1]
score = roc_auc_score(y_test, predictions)
printGreen("✔ ROC AUC score on test set: {0:.3f}".format(score))
return 0
# Create SGD Logistic Regression Classifier
sgd_log_reg_model = SGDClassifier(loss='log', penalty=None, fit_intercept=True,
n_iter=5, learning_rate='constant', eta0=0.01)
# Train Classifier
sgd_log_reg_model.fit(X_train_n, y_train_n)
printGreen('✔ Logistic regression model training complete..."\t\t{0:.1f}s'.format(time.time() - start))
# Run model on test set
predictions = sgd_log_reg_model.predict_proba(X_test)[:, 1]
# Evaluate model
score = roc_auc_score(y_test, predictions)
printGreen("✔ ROC AUC score on test set: {0:.3f}".format(score))
# Save model
l_reg_file = open('../models/logistic_regression_model.sav', "wb")
pickle.dump(sgd_log_reg_model, l_reg_file)
l_reg_file.close()
printGreen('✔ Logistic regression model saved...')
def split_data(df, status):
start = time.time()
df_train = df[df.invoicedate < '2011-10-09']
df_train = df_train.reset_index(drop=True)
df_val = df[(df.invoicedate >= '2011-10-09') &
(df.invoicedate <= '2011-11-09') ]
df_val = df_val.reset_index(drop=True)
df_test = df[df.invoicedate >= '2011-11-09']
df_test = df_test.reset_index(drop=True)
if (status):
printGreen('✔ Split Data\t\t{0:.1f}s'.format(time.time() - start))
# Pack data
return [df_train, df_test, df_val]
def main():
# Initial Message
printGreen("Click-through rate models training started...\n")
# Logistic Regression
printGreen('Logistic Regression')
logistic_regression(load_model=True)
print('\n')
# OL Logistic Regression
printGreen('Logistic Regressions using Online Learning')
logistic_regression_ol(load_model=True)
print('\n')
printGreen("✔ Done")
def logistic_regression_ol(load_model=True):
start = time.time()
if load_model == False:
printYellow(
"* Logistic regression (using online learning) model training started..."
)
# Build Classifier
og_reg_model = LogisticRegression()
# Training sets
X_dict_train, y_train = process_data()
dict_one_hot_encoder = DictVectorizer(sparse=False)
X_train = dict_one_hot_encoder.fit_transform(X_dict_train)
X_train_100k = X_train
y_train_100k = np.array(y_train)
# Test sets
X_dict_test, y_test_next10k = process_test_data()
X_test_next10k = dict_one_hot_encoder.transform(X_dict_test)
if load_model == True:
printGreen('✔ Loading model from previous training...')
l_reg_file = open('./models/logistic_regression_model_ol.sav', 'rb')
log_reg_model = pickle.load(l_reg_file)
predictions = log_reg_model.predict_proba(X_test_next10k)[:, 1]
score = roc_auc_score(y_test_next10k, predictions)
printGreen("✔ ROC AUC score on test set: {0:.3f}".format(score))
# Evaluate and run model on training data
f = open('logistic_regression_ol_base_bid.csv', 'w')
f.write('base_bid,clicks\n')
for base_bid in range(50, 90, 1):
score = bidding(predictions, base_bid)
#printGreen('✔ clicks on test set:' + str(score))
f.write(str(base_bid) + ',' + str(score) + '\n')
f.close()
printGreen('✔ clicks on test set:' + str(score))
return 0
'''
def logistic_regression(sample_size=100000, load_model=True):
start = time.time()
if load_model == False:
printYellow("* Logistic regression model training started...")
# Create Training Set
n = sample_size
X_dict_train, y_train = process_data()
dict_one_hot_encoder = DictVectorizer(sparse=False)
X_train = dict_one_hot_encoder.fit_transform(X_dict_train)
# Create Test Set
X_dict_test, y_test = process_test_data()
X_test = dict_one_hot_encoder.transform(X_dict_test)
X_train_n = X_train
y_train_n = np.array(y_train)
# Load model instead of training again
if load_model == True:
printGreen('✔ Loading model from previous training...')
l_reg_file = open('./models/logistic_regression_model.sav', 'rb')
log_reg_model = pickle.load(l_reg_file)
predictions = log_reg_model.predict_proba(X_test)[:, 1]
score = roc_auc_score(y_test, predictions)
printGreen("✔ ROC AUC score on test set: {0:.3f}".format(score))
# Evaluate and run model on training data
f = open('logistic_regression_base_bid.csv', 'w')
f.write('base_bid,clicks\n')
for base_bid in range(50, 90, 1):
score = bidding(predictions, base_bid)
#printGreen('✔ clicks on test set:' + str(score))
f.write(str(base_bid) + ',' + str(score) + '\n')
f.close()
printGreen('✔ clicks on test set:' + str(score))
return 0
'''
def main():
# Initial Message
printGreen("Click-through rate models training started...\n")
# Decision Tree
printGreen('Decision Tree')
decision_tree(load_model=True)
print('\n')
# Random Forest
printGreen('Random Forest')
random_forest(load_model=False)
print('\n')
# Logistic Regression
printGreen('SGD Based Logistic Regression')
logistic_regression(load_model=True)
print('\n')
# OL Logistic Regression
printGreen('Logistic Regressions using Online Learning')
logistic_regression_ol(load_model=True)
print('\n')
printGreen("✔ Done")
def recommender(customer_id, status):
# Start time
start = time.time()
if status:
printGreen('✔ RetailBox started..\t\t{0:.1f}s'.format(time.time() -
start))
start = time.time()
# Validate User Input
validate_customer_id(customer_id)
# Load Dataframe and create item_table, purchase matrix, etc.
data = preprocess_data_rec_engine(status=True)
item_table = data[0]
purchase_sparse_matrix = data[1]
customers = data[2]
products = data[3]
quantity = data[4]
if status:
printGreen('✔ Processed Data..\t\t{0:.1f}s'.format(time.time() -
start))
start = time.time()
# Split Data (Training/Test Split)
training_test_split_data = split_data_mask(purchase_sparse_matrix,
pct_test=0.2)
product_training_set = training_test_split_data[0]
product_test_set = training_test_split_data[1]
product_user_altered = training_test_split_data[2]
if status:
printGreen(
'✔ Split Data into Training and Test Sets..\t\t{0:.1f}s'.format(
time.time() - start))
start = time.time()
# Train Recommendation Engine on given algorithm
alpha = 15
recommender_vecs = implicit.alternating_least_squares(
(product_training_set * alpha).astype('double'),
factors=20,
regularization=0.1,
iterations=50)
user_vecs = recommender_vecs[0]
item_vecs = recommender_vecs[1]
customers_arr = np.array(customers)
products_arr = np.array(products)
if status:
printGreen('✔ Recommender System Training Done..\t\t{0:.1f}s'.format(
time.time() - start))
start = time.time()
# Lookup customer id
cid = lookup_customer_id(customer_id)
# Generate Recommendations for Customer
rec_output = rec_items(cid, product_training_set, user_vecs, item_vecs,
customers_arr, products_arr, item_table)
# Display Customer
df = pd.read_pickle('../data/final/df_final.pkl')
table_pickle_file = open('../data/final/df_customer_table.pkl', "rb")
customer_table = pickle.load(table_pickle_file)
table_pickle_file.close()
search_customer(customer_id, df, customer_table)
# Display Item Recommendations
recommended_items_list = list_rec(rec_output)
display_recommender_items(recommended_items_list)
def process_data(status):
start = time.time()
# Get file path for source dataset
file_path = '../data/processed/df_retail.bin'
df = None
# Check if processed data file exists, if not, process raw dataset
if os.path.exists(file_path) == False:
df = pd.read_excel('../data/raw/Online Retail.xlsx')
# # Save df into pickle file
with open('../data/processed/df_retail.bin', 'wb') as f_out:
pickle.dump(df, f_out)
with open('../data/processed/df_retail.bin', 'rb') as f_in:
df = pickle.load(f_in)
# Display import status
if status:
printGreen('✔ Imported Data\t\t{0:.1f}s'.format(time.time() - start))
# Clean Data, lowercase, remove "return" transaction, and unknown user transactions
start = time.time()
df.columns = df.columns.str.lower()
df = df[~df.invoiceno.astype('str').str.startswith('C')].reset_index(drop=True)
df.customerid = df.customerid.fillna(-1).astype('int32')
# Encode item IDs with integers
stockcode_values = df.stockcode.astype('str')
stockcodes = sorted(set(stockcode_values))
stockcodes = {c: i for (i, c) in enumerate(stockcodes)}
df.stockcode = stockcode_values.map(stockcodes).astype('int32')
# Display process status
if status:
printGreen('✔ Processed Data\t\t{0:.1f}s'.format(time.time() - start))
# Store Customer IDs in a table
start = time.time()
i = 0
counter = 0
while counter != 532620:
if df['customerid'][counter] not in customer_id and df['customerid'][counter] != -1 and df['customerid'][counter] != None:
customer_id[df['customerid'][counter]] = i
i += 1
counter += 1
# Save Customer ID Table
customer_id_storage = open('../data/final/df_customer_table_long.pkl', "wb")
pickle.dump(customer_id, customer_id_storage)
customer_id_storage.close()
# Display process status
if status:
printGreen('✔ Stored Customer Data in Table\t\t{0:.1f}s'.format(time.time() - start))
# Save final DF for quick access
df.to_pickle('../data/final/df_final.pkl')
return df
