# 设置pandas显示配置
pd.set_option('display.max_columns', 1000)
pd.set_option('display.width', 1000)
pd.set_option('display.max_colwidth', 1000)

train = pd.read_csv('data/train.tsv', sep='\t')
test = pd.read_csv('data/test.tsv', sep='\t')
# 查看数据集大小
print(train.shape)
print(test.shape)
# 查看数据集列名
print(train.dtypes)
# 查看数据集前几行
print(train.head())
# 查看商品价格信息
print(train.price.describe())

# 对价格进行对数变换，比较转换前和转换后的分布情况
plt.subplot(1, 2, 1)
(train['price']).plot.hist(bins=50, figsize=(20, 10), edgecolor='white', range=[0, 250])
plt.xlabel('price+', fontsize=17)
plt.ylabel('frequency', fontsize=17)
plt.tick_params(labelsize=15)
plt.title('Price Distribution - Training Set', fontsize=17)

plt.subplot(1, 2, 2)
np.log(train['price'] + 1).plot.hist(bins=50, figsize=(20, 10), edgecolor='white')
plt.xlabel('log(price+1)', fontsize=17)
plt.ylabel('frequency', fontsize=17)
plt.tick_params(labelsize=15)
plt.title('Log(Price) Distribution - Training Set', fontsize=17)
plt.show()

# 运费承担：大概有55%卖家承担运费
print(train.shipping.value_counts() / len(train))

# 看下运费不同情况下的价格变化(包邮的价格贵一些）
prc_shipBySeller = train.loc[train.shipping == 0, 'price']
prc_shipByBuyer = train.loc[train.shipping == 1, 'price']
fig, ax = plt.subplots(figsize=(20, 10))
ax.hist(np.log(prc_shipBySeller + 1), color='#8CB4E1', alpha=1.0, bins=50, label='Price when Seller pays Shipping')
ax.hist(np.log(prc_shipByBuyer + 1), color='#007D00', alpha=0.7, bins=50, label='Price when Buyer pays Shipping')
ax.set(title='Histogram Comparison', ylabel='% of Dataset in Bin')
plt.legend()
plt.xlabel('log(price+1)', fontsize=17)
plt.ylabel('frequency', fontsize=17)
plt.title('Price Distribution by Shipping Type', fontsize=17)
plt.tick_params(labelsize=15)
plt.show()

# 商品类别划分
print('There are %d unique values in the category column' % train['category_name'].nunique())
print(train['category_name'].value_counts()[:5])
print('There are %d items that do not have a label' % train['category_name'].isnull().sum())

# 商品类别太多了，合并一下
def split_cat(text):
    try:
        return text.split("/")
    except:
        return "No Label", "No Label", "No Label"


train['general_cat'], train['subcat_1'], train['subcat_2'] = zip(*train['category_name'].apply(lambda x: split_cat(x)))
print(train.head())
test['general_cat'], test['subcat_1'], test['subcat_2'] = zip(*test['category_name'].apply(lambda x: split_cat(x)))
print('There are %d unique general_cat' % train['general_cat'].nunique())
print('There are %d unique first sub-categories' % train['subcat_1'].nunique())
print('There are %d unique second sub-categories' % train['subcat_2'].nunique())

# 主类别分布情况
x = train['general_cat'].value_counts().index.values.astype('str')
y = train['general_cat'].value_counts().values
pct = [('%.2f' % (v * 100)) + '%' for v in (y / len(train))]
tracel = go.Bar(x=x, y=y, text=pct)
layout = dict(title="Number of Items by Main Category", yaxis=dict(title='Count'), xaxis=dict(title='Category'))
fig = dict(data=[tracel], layout=layout)
py.iplot(fig)

# 前15个子类别分布情况
x = train['subcat_1'].value_counts().index.values.astype('str')[:15]
y = train['subcat_1'].value_counts().values[:15]
pct = [('%.2f' % (v * 100)) + '%' for v in (y / len(train))][:15]
tracel = go.Bar(x=x, y=y, text=pct, marker=dict(color=y, colorscale='Portland', showscale=True, reversescale=False))
layout = dict(title="Number of Items by Sub Category(Top 15)", yaxis=dict(title='Count'),
              xaxis=dict(title='SubCategory'))
fig = dict(data=[tracel], layout=layout)
py.iplot(fig)

# 不同类型商品价格浮动区间
general_cats = train['general_cat'].unique()
x = [train.loc[train['general_cat'] == cat, 'price'] for cat in general_cats]
data = [go.Box(x=np.log(x[i] + 1), name=general_cats[i]) for i in range(len(general_cats))]
layout = dict(title='Price Distribution by General Category', yaxis=dict(title='Frequency'),
              xaxis=dict(title='Category'))
fig = dict(data=data, layout=layout)
py.iplot(fig)

# 前10品牌名称的数据分布
x = train['brand_name'].value_counts().index.values.astype('str')[:10]
y = train['brand_name'].value_counts().values[:10]
tracel = go.Bar(x=x, y=y, marker=dict(color=y, colorscale='Portland', showscale=True, reversescale=False))
layout = dict(title="Top 10 Brand by Number of Items", yaxis=dict(title='Count'), xaxis=dict(title='Brand Name'))
fig = dict(data=[tracel], layout=layout)
py.iplot(fig)

# 商品描述对价格的影响
def wordCount(text):
    try:
        text = text.lower()
        regex = re.compile('[' + re.escape(string.punctuation) + '0-9\\r\\t\\n]')
        txt = regex.sub(' ', text)
        words = [w for w in txt.split(" ") if w not in stop_words.STOP_WORDS and len(w) > 3]
        return len(words)
    except:
        return 0


train['desc_len'] = train['item_description'].apply(lambda x: wordCount(x))
test['desc_len'] = test['item_description'].apply(lambda x: wordCount(x))
print(train.head())

df = train.groupby('desc_len')['price'].mean().reset_index()
tracel = go.Scatter(x=df['desc_len'], y=np.log(df['price'] + 1), mode='lines+markers', name='lines+markers')
layout = dict(title='Average Log(Price) by Description Length', yaxis=dict(title='Average Log(Price)'),
              xaxis=dict(title='Description Length'))
fig = dict(data=[tracel], layout=layout)
py.iplot(fig)

print(train.item_description.isnull().sum())
# 去掉缺失值
train = train[pd.notnull(train['item_description'])]
# 提取每种品牌的描述关键词
tokenize = nltk.data.load('tokenizers/punkt/english.pickle')
cat_desc = dict()
for cat in general_cats:
    text = ' '.join(train.loc[train['general_cat'] == cat, 'item_description'].values)
    cat_desc[cat] = tokenize.tokenize(text)

# 统计常用关键词
flat_lst = [item for sublist in list(cat_desc.values()) for item in sublist]
allWordsCount = Counter(flat_lst)
all_top10 = allWordsCount.most_common(20)
x = [w[0] for w in all_top10]
y = [w[1] for w in all_top10]
tracel = go.Bar(x=x, y=y)
layout = dict(title='Word Frequency', yaxis=dict(title='Count'), xaxis=dict(title='Word'))
fig = dict(data=[tracel], layout=layout)
py.iplot(fig)

# 展示不同商品的关键词
stop = set(stopwords.words('english'))


def tokenize(text):
    try:
        regex = re.compile('[' + re.escape(string.punctuation) + '0-9\\r\\t\\n]')
        text = regex.sub(' ', text)

        tokens_ = [word_tokenize(s) for s in sent_tokenize(text)]
        tokens = []
        for token_by_sent in tokens_:
            tokens += token_by_sent
        tokens = list(filter(lambda t: t.lower() not in stop, tokens))
        filtered_tokens = [w for w in tokens if re.search('[a-zA-Z]', w)]
        filtered_tokens = [w.lower() for w in filtered_tokens if len(w) >= 3]
        return filtered_tokens
    except TypeError as e:
        print(text, e)


train['tokens'] = train['item_description'].map(tokenize)
test['tokens'] = test['item_description'].map(tokenize)

train.reset_index(drop=True, inplace=True)
test.reset_index(drop=True, inplace=True)
for description, tokens in zip(train['item_description'].head(), train['tokens'].head()):
    print('description:', description)
    print('tokens:', tokens)
    print()

cat_desc = dict()
for cat in general_cats:
    text = ' '.join(train.loc[train['general_cat'] == cat, 'item_description'].values)
    cat_desc[cat] = tokenize(text)

cat_desc100 = dict()
for key, value in cat_desc.items():
    cat_desc100[key] = Counter(value).most_common()


def generate_wordcloud(tup):
    wordcloud = WordCloud(background_color='white', max_words=50, max_font_size=40, random_state=42).generate(str(tup))
    return wordcloud


fig, axes = plt.subplots(len(cat_desc100) // 2 + 1, 2, figsize=(30, 15))

for i, (key, cat) in enumerate(cat_desc100.items()):
    ax = axes[i // 2, i % 2]
    ax.imshow(generate_wordcloud(cat), interpolation='bilinear')
    ax.axis('off')
    ax.set_title("%s Top 100" % key, fontsize=12)

plt.show()

# tf-idf
vectorizer = TfidfVectorizer(min_df=10, max_features=180000, tokenizer=tokenize, ngram_range=(1, 2))
all_desc = np.append(train['item_description'].values, test['item_description'].values)
vz = vectorizer.fit_transform(list(all_desc))
print(vz.shape)

tfidf = dict(zip(vectorizer.get_feature_names_out(), vectorizer.idf_))
tfidf = pd.DataFrame(columns=['tfidf']).from_dict(dict(tfidf), orient='index')
tfidf.columns = ['tfidf']
print(tfidf.sort_values(by=['tfidf'], ascending=True).head(10))
print(tfidf.sort_values(by=['tfidf'], ascending=False).head(10))

trn = train.copy()
tst = test.copy()
trn['is_train'] = 1
tst['is_train'] = 0
sample_sz = 15000
# 采样
combined_df = pd.concat([trn, tst])
combined_sample = combined_df.sample(n=sample_sz)
vz_sample = vectorizer.fit_transform(list(combined_sample['item_description']))
# SVD 降维
n_comp = 30
svd = TruncatedSVD(n_components=n_comp, random_state=42)
svd_tfidf = svd.fit_transform(vz_sample)
# t-SNE降维
tsne_model = TSNE(n_components=2, verbose=1, random_state=42, n_iter=500)
tsne_tfidf = tsne_model.fit_transform(svd_tfidf)

plot_tfidf = bp.figure(width=700, height=600, title='tf-idf clustring of the item description',
                       tools='pan, wheel_zoom, box_zoom, reset, hover', x_axis_type=None, y_axis_type=None,
                       min_border=1)
combined_sample.reset_index(inplace=True, drop=True)
tfidf_df = pd.DataFrame(tsne_tfidf, columns=['x', 'y'])
tfidf_df['description'] = combined_sample['item_description']
tfidf_df['tokens'] = combined_sample['tokens']
tfidf_df['category'] = combined_sample['general_cat']
plot_tfidf.scatter(x='x', y='y', source=tfidf_df, alpha=0.7)
hover = plot_tfidf.select(dict(type=HoverTool))
hover.tooltips = {'description': '@description', 'tokens': '@tokens', 'category': '@category'}
show(plot_tfidf)

# 聚类分堆
num_clusters = 10
kmeans_model = MiniBatchKMeans(n_clusters=num_clusters, init='k-means++', n_init=1, init_size=10000, batch_size=1000,
                               verbose=0, max_iter=1000)
kmeans_model.fit(vz_sample)
kmeans_clusters = kmeans_model.predict(vz_sample)
kmeans_distances = kmeans_model.transform(vz_sample)
tsne_kmeans = tsne_model.fit_transform(kmeans_distances)

kmeans_df = pd.DataFrame(tsne_kmeans, columns=['x', 'y'])
kmeans_df['cluster'] = kmeans_clusters
kmeans_df['description'] = combined_sample['item_description']
kmeans_df['category'] = combined_sample['general_cat']
plot_kmeans = bp.figure(width=700, height=600, title='KMeans clustering of the description',
                        tools='pan, wheel_zoom, box_zoom, reset, hover', x_axis_type=None, y_axis_type=None,
                        min_border=1)
print(kmeans_clusters)
colormap = {'0': 'red', '1': 'green', '2': 'blue', '3': 'black', '4': 'yellow', '5': 'pink', '6': 'purple', '7': 'grey',
            '8': 'brown', '9': 'orange'}


def get_color(num):
    if num == 0:
        return 'red'
    elif num == 1:
        return 'green'
    elif num == 2:
        return 'blue'
    elif num == 3:
        return 'black'
    elif num == 4:
        return 'yellow'
    elif num == 5:
        return 'pink'
    elif num == 6:
        return 'purple'
    elif num == 7:
        return 'grey'
    elif num == 8:
        return 'brown'
    elif num == 9:
        return 'orange'


color = pd.Series(kmeans_clusters).apply(get_color)
source = ColumnDataSource(
    data=dict(x=kmeans_df['x'], y=kmeans_df['y'], color=color, description=kmeans_df['description'],
              category=kmeans_df['category'], cluster=kmeans_df['cluster']))
plot_kmeans.scatter(x='x', y='y', color='color', source=source)
hover = plot_kmeans.select(dict(type=HoverTool))
hover.tooltips = {'description': '@description', 'category': '@category', 'cluster': '@cluster'}
show(plot_kmeans)

# LDA分堆
cvectorizer = CountVectorizer(min_df=4, max_features=180000, tokenizer=tokenize, ngram_range=(1, 2))
cvz = cvectorizer.fit_transform(combined_sample['item_description'])
lda_model = LatentDirichletAllocation(n_components=10, learning_method='online', max_iter=20, random_state=42)
X_topics = lda_model.fit_transform(cvz)

# 获取主题
n_top_words = 10
topic_summaries = []
topic_word = lda_model.components_
vocab = cvectorizer.get_feature_names_out()
for i, topic_dist in enumerate(topic_word):
    topic_words = np.array(vocab)[np.argsort(topic_dist)][:-(n_top_words + 1):-1]
    topic_summaries.append(' '.join(topic_words))
    print('Topic {}:{}'.format(i, '|'.join(topic_words)))

tsne_lda = tsne_model.fit_transform(X_topics)
unnormalized = np.matrix(X_topics)
doc_topic = unnormalized / unnormalized.sum(axis=1)
lda_keys = []
for i, tweet in enumerate(combined_sample['item_description']):
    lda_keys += [doc_topic[i].argmax()]

lda_df = pd.DataFrame(tsne_lda, columns=['x', 'y'])
lda_df['description'] = combined_sample['item_description']
lda_df['category'] = combined_sample['general_cat']
lda_df['topic'] = lda_keys
lda_df['topic'] = lda_df['topic'].map(int)
plot_lda = bp.figure(width=700, height=600, title='LDA topic visualization',
                     tools='pan, wheel_zoom, box_zoom, reset, hover', x_axis_type=None, y_axis_type=None, min_border=1)
source = ColumnDataSource(
    data=dict(x=lda_df['x'], y=lda_df['y'], color=color, description=lda_df['description'],
              topic=lda_df['topic'], category=lda_df['category']))
plot_lda.scatter(x='x', y='y', color='color', source=source)
hover = plot_lda.select(dict(type=HoverTool))
hover.tooltips = {'description': '@description', 'topic': '@topic', 'category': '@category'}
show(plot_lda)