def classify(f, verbose=True):
"""
Function that takes as input a csv file containing a list of transactions
and returns the list of clients along with the cluster they belong to.
:param f: Path of the file containing the transactions
:param verbose: If True, will show the result of the classification
:return: List of clients, with their cluster group
"""
df = pd.read_csv(f, sep=';')
df['Price'] = df['Quantity'] * df['UnitPrice']
df['InvoiceDate'] = df['InvoiceDate'].apply(
lambda x: datetime.datetime.strptime(x, '%d/%m/%Y'))
df['Cancelled'] = df['InvoiceNo'].astype(str).str.startswith('C')
# Functions useful to create the clients table
def first(x):
return (datetime.datetime(2011, 12, 10) - x.min()).days
def last(x):
return (datetime.datetime(2011, 12, 10) - x.max()).days
def unique(x):
return len(x.unique())
def cancel_rate(x):
return x.sum() / (len(x) - x.sum())
# Add recency, days since first transaction and number of transactions (frequency)
temp = df[~df.Cancelled]
customers = temp.groupby('CustomerID').agg({
'InvoiceDate': [last, first],
'InvoiceNo': unique,
})
customers.columns = ['_'.join(x) for x in customers.columns.ravel()]
customers.rename(columns={
'InvoiceDate_last': 'recency',
'InvoiceDate_first': 'client_since',
'InvoiceNo_unique': 'frequency'
},
inplace=True)
# Add mean quantity, total revenue and cancel rate
temp = df.groupby('CustomerID').agg({
'Quantity': 'mean',
'Price': lambda x: x.sum(),
'Cancelled': cancel_rate,
})
customers = pd.merge(customers, temp, left_index=True, right_index=True)
customers.rename(columns={
'Quantity': 'quantity_mean',
'Price': 'monetary_value',
'Cancelled': 'cancel_rate'
},
inplace=True)
customers = customers[(customers.monetary_value >= 1)]
# Loading parameters
with open('params.pickle', 'rb') as f:
params = pickle.load(f)
std_scaler = StandardScaler()
std_scaler.mean_ = params['mean_']
std_scaler.var_ = params['var_']
std_scaler.scale_ = params['scale_']
lr = OneVsOneClassifier(params['estimator'])
lr.estimators_ = params['estimators_']
lr.classes_ = params['classes_']
lr.pairwise_indices_ = None
customers_std = std_scaler.transform(customers)
y_pred = lr.predict(customers_std)
temp = customers.copy()
temp['cluster'] = y_pred
temp['cluster_name'] = temp['cluster'].apply(
lambda x: params['class_names'][x])
if verbose:
print(temp['cluster_name'])
return temp['cluster_name']
