customer2vec.qmd

---
jupyter: python3
---

# Customer2Vec

In this notebook, we demonstrate how to learn embeddings for customers from transactional data using Doc2Vec. Each customer is viwed as a document, orders as sentences, and products as words. We also compare Customer2Vec with the baseline representations obtained using k-means on manually designed features.

### Data
We use an external dataset from the Instacart Market Basket Analysis competition (see [datasets](https://github.com/ikatsov/tensor-house/blob/master/resources/datasets.md)). Please download and unzip the dataset to 'data' folder before running this notebook.

### References
1. Arora S., Warrier D. -- Decoding Fashion Contexts Using Word Embeddings, 2016
1. Stiebellehner S., Wang J, Yuan S. -- Learning Continuous User Representations through Hybrid Filtering with doc2vec, 2017

```{python}
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
from tqdm.notebook import tqdm
import numpy as np
import os
from sklearn.manifold import TSNE

from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_samples, silhouette_score
from sklearn import preprocessing

pd.options.display.max_rows = 20
#matplotlib inline

pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
pd.set_option('display.max_colwidth', 500)

from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"

import glob
from IPython.display import display, HTML

import multiprocessing as mp
print('Number of CPU cores:', mp.cpu_count())
```

# Step 1: Data Exploration and Preparation

```{python}
DATA_PATH = 'data/instacart'
files_list = glob.glob(f'{DATA_PATH}/*.csv')

data_dict = {}

for file in files_list:
    print(f'Reading: {file}')
    data = pd.read_csv(file)
    print(data.info(show_counts=True))
    data_dict[file.split('.')[0].split('/')[-1]] = data

print(f'Loaded data sets: {data_dict.keys()}')
```

# Step 2: Establish the Baselines

We start with designing several basic features and segmenting customers using this basic representation. Note that the dataset is limited and does not allow for advanced ferature engineering (there is no demographic data, etc). 

### Step 2.1: Feature Engineering

```{python}
train_orders = data_dict['order_products__train']
prior_orders = data_dict['order_products__prior']
products = data_dict['products'].set_index('product_id')

orders = data_dict['orders']
prior_orders = prior_orders.merge(right=orders[['user_id','order_id','order_number']],on='order_id',how='left')

prior_orders.head()
```

```{python}
# Sample users to keep the problem computationaly tractable 
USER_SUBSET = 50000
user_ids_sample = prior_orders['user_id'].sample(n=USER_SUBSET, replace=False)
```

```{python}
prior_orders_details = prior_orders[prior_orders.user_id.isin(user_ids_sample)].copy()
prior_orders_details['product_id'] = prior_orders_details['product_id'].astype(int)
prior_orders_details = prior_orders_details.merge(data_dict['products'], on='product_id', how='left')
prior_orders_details = prior_orders_details.merge(data_dict['aisles'], on='aisle_id', how='left')
prior_orders_details = prior_orders_details.merge(data_dict['departments'], on='department_id', how='left')

prior_orders_details.head()
```

```{python}
# Create basic user features: relative purchase frequences in each depertment/aisle

feature_department = pd.pivot_table(prior_orders_details, index=['user_id'], values=['product_id'], columns=['department'], aggfunc='count', fill_value=0)
feature_department = feature_department.div(feature_department.sum(axis=1), axis=0)
feature_department.columns = feature_department.columns.droplevel(0)
feature_department = feature_department.reset_index()

feature_aisle = pd.pivot_table(prior_orders_details, index=['user_id'], values=['product_id'], columns=['aisle'], aggfunc='count', fill_value=0)
feature_aisle = feature_aisle.div(feature_aisle.sum(axis=1), axis=0)
feature_aisle.columns = feature_aisle.columns.droplevel(0)
feature_aisle = feature_aisle.reset_index()

feature_df = feature_department.merge(feature_aisle, how='left', on='user_id').set_index('user_id')

feature_df.iloc[:5, :10] # show first 10 columns (departments) only
```

```{python}
len(feature_df.columns)
```

### Step 2.2: Visualize the Basic Space Using t-SNE  

```{python}
from sklearn.preprocessing import MinMaxScaler

mm_scale = MinMaxScaler()
feature_df_scale = pd.DataFrame(mm_scale.fit_transform(feature_df),
                                columns=feature_df.columns,
                                index=feature_df.index.values)

tsne_doc_features = TSNE(n_components=2, verbose=1, perplexity=30, n_iter=500)
tsne_features_doc = tsne_doc_features.fit_transform(feature_df_scale.values)
```

```{python}
tsne_doc_features = pd.DataFrame({'user_id':feature_df.index.values})
tsne_doc_features['tsne-2d-one'] = tsne_features_doc[:,0]
tsne_doc_features['tsne-2d-two'] = tsne_features_doc[:,1]
```

```{python}
plt.figure(figsize=(16, 16))
sns.scatterplot(
    x="tsne-2d-one", y="tsne-2d-two",
    data=tsne_doc_features,
    legend="full",
    alpha=0.3
)
plt.show()
```

```{python}
# Computing silhouette scores for different clustering option
silhouette_list = []
for k in tqdm(range(2, 12, 2)):
    clusters = KMeans(n_clusters=k, n_init = 10).fit(feature_df_scale).labels_.astype(float)
    silhouette_avg = silhouette_score(feature_df_scale, clusters, metric="euclidean")
    silhouette_list.append(silhouette_avg)
    print(f'Silhouette score for {k} clusters is : {silhouette_avg:.4}')
```

```{python}
plt.figure(figsize=(10, 6))
plt.plot(range(2, 12, 2), silhouette_list)
```

Conclusions: We can see from the t-SNE visualization it is possible to detect 2-3 clusters, but the silhouette score is close to zero and this feature space is not a good choice for customer segmentation. 
We should develop more advanced features and incorporate more domain knowledge to obtain better feature space for segmentation.

# Step 3: Customer2Vec Using Doc2Vec

In this and next section, we are learning and exploring the customer semantic space using Doc2Vec.

### Step 3.1: Data Preparation

```{python}
train_orders["product_id"] = train_orders["product_id"].astype(str)
prior_orders["product_id"] = prior_orders["product_id"].astype(str)

# It is important to sort order and products chronologically
prior_orders.sort_values(by=['user_id','order_number','add_to_cart_order'], inplace=True)
```

```{python}
combined_orders_by_user_id = prior_orders.groupby("user_id").apply(lambda order: ' '.join(order['product_id'].tolist()))

combined_orders_by_user_id = pd.DataFrame(combined_orders_by_user_id,columns=['all_orders'])
print(f'Number of orders: {combined_orders_by_user_id.shape[0]}')
combined_orders_by_user_id.reset_index(inplace=True)
combined_orders_by_user_id.user_id = combined_orders_by_user_id.user_id.astype(str)

combined_orders_by_user_id.head()
```

### Step 3.2: Train Doc2Vec model

We use implementation from Gensim adjusting the following parameters:
* documents = users (each user is represented by a chronologically sorted list of products)
* window - Maximum distance between the current and predicted word within a sentence.
* vector_size - Dimensionality of the word vectors.
* min_count - Ignores all words with total frequency lower than this.

Note that model training can take a long time (10+ hours).

```{python}
TRAIN_USER_MODEL = False    # True - create a new model, False - load a previosuly created model
MODEL_DIR = 'models'

if not os.path.exists(MODEL_DIR):
    os.makedirs(MODEL_DIR)
```

```{python}
from gensim.models.doc2vec import TaggedDocument, Doc2Vec
import timeit
import gensim

embeddings_dim = 200    # dimensionality of user representation

filename = f'models/customer2vec.{embeddings_dim}d.model'
if TRAIN_USER_MODEL:

    class TaggedDocumentIterator(object):
        def __init__(self, df):
           self.df = df
        def __iter__(self):
            for row in self.df.itertuples():
                yield TaggedDocument(words=dict(row._asdict())['all_orders'].split(),tags=[dict(row._asdict())['user_id']])

    it = TaggedDocumentIterator(combined_orders_by_user_id)

    doc_model = Doc2Vec(vector_size=embeddings_dim, 
                        window=5, 
                        min_count=10, 
                        workers=mp.cpu_count(),
                        alpha=0.055, 
                        min_alpha=0.055,
                        epochs=120)   # use fixed learning rate

    train_corpus = list(it)

    doc_model.build_vocab(train_corpus)

    for epoch in tqdm(range(10)):
        doc_model.alpha -= 0.005                    # decrease the learning rate
        doc_model.min_alpha = doc_model.alpha       # fix the learning rate, no decay
        doc_model.train(train_corpus, total_examples=doc_model.corpus_count, epochs=doc_model.epochs)
        print('Iteration:', epoch)

    doc_model.save(filename)
    print(f'Model saved to [{filename}]')
    
else:
    doc_model = Doc2Vec.load(filename)
    print(f'Model loaded from [{filename}]')
```

```{python}
vocab_doc = list(doc_model.dv.doctags.keys())
doc_vector_dict = {arg:doc_model.dv[arg] for arg in vocab_doc}
X_doc = pd.DataFrame(doc_vector_dict).T.values


X_doc.shape, len(vocab_doc), prior_orders["user_id"].nunique()
```

### Step 3.3: Visualize the Customer Semantic Space Using t-SNE

```{python}
user_ids_sample_str = set([str(id) for id in user_ids_sample])
idx = []
for i, user_id in enumerate(doc_vector_dict):
    if user_id in user_ids_sample_str:
        idx.append(i)
X_doc_subset = X_doc[idx] # only sampled user IDs
X_doc_subset.shape
```

```{python}
doc_vec_subset = pd.DataFrame(doc_vector_dict).T.iloc[idx]
doc_vec_subset.shape
```

```{python}
from sklearn.metrics import pairwise_distances

distance_matrix_doc = pairwise_distances(X_doc_subset, X_doc_subset, metric='cosine', n_jobs=-1)
tsne_doc = TSNE(metric="precomputed", n_components=2, verbose=1, perplexity=30, n_iter=500)
tsne_results_doc = tsne_doc.fit_transform(distance_matrix_doc)
```

```{python}
tsne_doc = pd.DataFrame()
tsne_doc['tsne-2d-one'] = tsne_results_doc[:,0]
tsne_doc['tsne-2d-two'] = tsne_results_doc[:,1]
```

```{python}
plt.figure(figsize=(16,16))
sns.scatterplot(
    x="tsne-2d-one", y="tsne-2d-two",
    data=tsne_doc,
    legend="full",
    alpha=0.3
)
plt.show()
```

### Step 3.4: Segmentation in the Customer Semantic Space Using t-SNE

```{python}
def cluster_cosine(X, k):
    # normalization is equivalent to cosine distance
    return KMeans(n_clusters=k).fit(preprocessing.normalize(X_doc_subset)).labels_.astype(float)

silhouette_list = []
for k in tqdm(range(2, 22, 1)):
    latent_clusters = cluster_cosine(X_doc_subset, k)
    silhouette_avg = silhouette_score(X_doc_subset, latent_clusters, metric="cosine")
    silhouette_list.append(silhouette_avg)
    print(f'Silhouette score for {k} clusters is : {silhouette_avg:.4}')
```

```{python}
plt.figure(figsize=(10,6))
plt.plot(range(2,22,1), silhouette_list);
```

```{python}
N_CLUSTER = 12

latent_clusters = cluster_cosine(X_doc_subset, N_CLUSTER)
doc_vec_end = doc_vec_subset.copy()
doc_vec_end['label'] = latent_clusters
tsne_doc['cluster'] = latent_clusters

doc_vec_end['label'].value_counts()
```

```{python}
plt.figure(figsize=(16, 16))
sns.scatterplot(
    x="tsne-2d-one", y="tsne-2d-two",
    hue='cluster',
    palette=sns.color_palette("hls", tsne_doc['cluster'].nunique()),
    data=tsne_doc,
    legend="full",
    alpha=0.3
);
plt.show();
```

```{python}
feature_df['latent_cluster'] = latent_clusters

department_names = np.setdiff1d(prior_orders_details['department'].unique(), ['other', 'missing'])
interpetation_department = feature_df.groupby('latent_cluster')[department_names].mean()

interpetation_department.T.div(interpetation_department.sum(axis=1)).round(3)
```

```{python}
interpetation_aisle = feature_df.groupby('latent_cluster')[feature_df.columns.values[16:-1]].mean()
interpetation_aisle.T.div(interpetation_aisle.sum(axis=1)).round(3).head(20)
```

```{python}
prior_orders_details_clustered = prior_orders_details.copy()
prior_orders_details_clustered = prior_orders_details_clustered.merge(feature_df['latent_cluster'], on='user_id', how='left')

for cluster_id in [7.0, 3.0]:
    prior_orders_details_clustered[prior_orders_details_clustered['latent_cluster']==cluster_id][['user_id', 'product_name']].groupby("user_id").apply(lambda order: ' > '.join(order['product_name'])).reset_index().head(5)
```

Conclusions: Cluster 7 is clearly characterized by healthy products (vegetables, nonfat dairy, vitamins, etc). Cluster 3 has significant percentage of high-calorie products (sausage, bananas, frozen desserts, ice cream, pasta sause, etc).