def __init__(self, id_path, con_path, char_type):
self.file_io = FileIO()
self.encode = CategoryEncode()
self.count_rec = CountRecord()
self.extract_col = ExtractColumns()
self.bin = Binning()
self.ss = Scaler()
# ファイルオープン
self.id = self.file_io.open_file_as_pandas(id_path, char_type)
self.con = self.file_io.open_file_as_pandas(con_path, char_type)
def __init__(
self,
id_path,
cust_payment_path,
cust_attr_path,
target_attr_path,
average_attr_path,
cust_path,
cancel_path,
contact_path,
cti_path,
register_type_path,
status_path,
stay_time_path,
pv_sum_path,
session_path,
shop_path,
pref_path,
char_type):
self.file_io = FileIO()
self.encode = CategoryEncode()
self.count_rec = CountRecord()
self.extract_col = ExtractColumns()
self.bin = Binning()
# ファイルオープン
self.id = self.file_io.open_file_as_pandas(id_path,char_type)
self.cust_payment = self.file_io.open_file_as_pandas(cust_payment_path, char_type)
self.cust_attr = self.file_io.open_file_as_pandas(cust_attr_path, char_type)
self.target_attr = self.file_io.open_file_as_pandas(target_attr_path, char_type)
self.average_attr = self.file_io.open_file_as_pandas(average_attr_path, char_type)
self.cust = self.file_io.open_file_as_pandas(cust_path, char_type)
self.cancel = self.file_io.open_file_as_pandas(cancel_path, char_type)
self.contact = self.file_io.open_file_as_pandas(contact_path, char_type)
self.cti = self.file_io.open_file_as_pandas(cti_path, char_type)
self.register_type = self.file_io.open_file_as_pandas(register_type_path, char_type)
self.status = self.file_io.open_file_as_pandas(status_path, char_type)
self.stay_time = self.file_io.open_file_as_pandas(stay_time_path, char_type)
self.pv_sum = self.file_io.open_file_as_pandas(pv_sum_path, char_type)
self.session = self.file_io.open_file_as_pandas(session_path, char_type)
self.shop = self.file_io.open_file_as_pandas(shop_path, char_type)
self.pref = self.file_io.open_file_as_pandas(pref_path, char_type)
class PreprocClassify:
def __init__(self, id_path, con_path, char_type):
self.file_io = FileIO()
self.encode = CategoryEncode()
self.count_rec = CountRecord()
self.extract_col = ExtractColumns()
self.bin = Binning()
self.ss = Scaler()
# ファイルオープン
self.id = self.file_io.open_file_as_pandas(id_path, char_type)
self.con = self.file_io.open_file_as_pandas(con_path, char_type)
def make_class_data(self, out_path):
'''目的変数を識別し、分析対象ファイルにマージする'''
# 売上<=0を削除
#org_df = self.con.drop(self.con[self.con['売上']<=0].index)
# 目的変数列を抽出
cust_attr_col_list = [] # 抽出列リストを初期化
cust_attr_tg_list = ['売上'] # 抽出列リストに目的変数列を追加
cust_con_col = self.extract_col.extract(self.con,
self.con['顧客ID'],
extract_col=cust_attr_tg_list)
# 不要な顧客ID列を削除
cust_con_col = cust_con_col.drop(['顧客ID'], axis=1)
# 欠損値をゼロうめ
cust_con_col = cust_con_col.fillna(0)
# 抽出した目的変数列に対して、標準化（平均0, 分散1）処理を行う
std_cust_con_col = self.ss.sl_standard_scaler(cust_con_col,
data_type='float')
# 標準化された目的変数列を平均より上か下かで識別
type_bins = [-1, 0, 1] # 範囲:(-1,1), 0で分割する
type_bin_label_list = [0, 1] # 0より小: low, 0より大: high
type_col = self.bin.list_divide(std_cust_con_col['売上'], type_bins,
type_bin_label_list) # 分類用データの生成
type_df = pd.DataFrame(
data=type_col,
index=std_cust_con_col.index) # 分類用データ（numpy）をdataframeに変更
type_df.columns = ['クラス'] # dataframeのカラム名を変更
# 分類用データを既存の分析用データにマージ
type_df = pd.concat([self.id, type_df], axis=1) # id列とtype_dfを連結
con = pd.merge(self.con, type_df, on='顧客ID',
how='left') # 既存dataframeとtype_dfを連結
# 書き出し処理
self.file_io.export_csv_from_pandas(con, out_path)
class ConcatCsvs:
def __init__(self, id_path, cust_payment_path, cust_attr_path,
target_attr_path, average_attr_path, cust_path, cancel_path,
contact_path, cti_path, register_type_path, status_path,
stay_time_path, pv_sum_path, session_path, shop_path,
pref_path, char_type):
self.file_io = FileIO()
self.encode = CategoryEncode()
self.count_rec = CountRecord()
self.extract_col = ExtractColumns()
self.bin = Binning()
# ファイルオープン
self.id = self.file_io.open_file_as_pandas(id_path, char_type)
self.cust_payment = self.file_io.open_file_as_pandas(
cust_payment_path, char_type)
self.cust_attr = self.file_io.open_file_as_pandas(
cust_attr_path, char_type)
self.target_attr = self.file_io.open_file_as_pandas(
target_attr_path, char_type)
self.average_attr = self.file_io.open_file_as_pandas(
average_attr_path, char_type)
self.cust = self.file_io.open_file_as_pandas(cust_path, char_type)
self.cancel = self.file_io.open_file_as_pandas(cancel_path, char_type)
self.contact = self.file_io.open_file_as_pandas(
contact_path, char_type)
self.cti = self.file_io.open_file_as_pandas(cti_path, char_type)
self.register_type = self.file_io.open_file_as_pandas(
register_type_path, char_type)
self.status = self.file_io.open_file_as_pandas(status_path, char_type)
self.stay_time = self.file_io.open_file_as_pandas(
stay_time_path, char_type)
self.pv_sum = self.file_io.open_file_as_pandas(pv_sum_path, char_type)
self.session = self.file_io.open_file_as_pandas(
session_path, char_type)
self.shop = self.file_io.open_file_as_pandas(shop_path, char_type)
self.pref = self.file_io.open_file_as_pandas(pref_path, char_type)
def concat(self, out_path, out_path2):
# 特徴量抽出処理
# cust_payment
# カテゴリーデータなし
# --- check ---
#print("--- cust_payment shape ---\n {}\n".format(self.cust_payment.shape))
#print(self.cust_payment.head())
# cust_attr
cust_attr_col_list = []
cust_attr_tg_list = [
'指名回数', 'コース受諾回数', '紹介カード受渡回数', '治療送客回数', '院長挨拶回数'
]
# カテゴリ列を抽出
cust_attr_category_col = self.extract_col.extract(
self.cust_attr,
self.cust_attr['顧客ID'],
extract_col=cust_attr_tg_list)
# 非カテゴリ列を抽出
cust_attr_non_category_col = self.extract_col.exclude(
self.cust_attr, exclude_col=cust_attr_tg_list)
# 特徴量抽出
org_cust_attr = self.encode.transform_feature(
cust_attr_category_col, aggregate_col=cust_attr_tg_list)
org_cust_attr = org_cust_attr.fillna(0)
#org_cust_attr = org_cust_attr.drop('Unnamed: 0', axis=1)
# ラベル付与
for col in cust_attr_tg_list:
cust_attr_col_list += self.encode.transform_label(
self.cust_attr[col], col)
else:
cust_attr_col_list += ['顧客ID']
# ラベル設定
org_cust_attr.columns = cust_attr_col_list
# 集計処理
feat_cust_attr = self.count_rec.group_sum(
org_cust_attr, index_col='顧客ID', aggregate_col=cust_attr_col_list)
# カテゴリ列と非カテゴリ列を結合
feat_cust_attr = pd.merge(feat_cust_attr,
cust_attr_non_category_col,
on='顧客ID',
how='left')
feat_cust_attr = feat_cust_attr.drop('Unnamed: 0', axis=1)
# --- check ---
#print("--- feat_cust_attr shape ---\n {}\n".format(feat_cust_attr.shape))
#print(feat_cust_attr.head())
#self.file_io.export_csv_from_pandas(feat_cust_attr, './data/out/mid_feat_cust_attr.csv')
# product_attr
'''
product_attr_col_list = []
product_attr_tg_list = ['商品コード']
# カテゴリ列を抽出
product_attr_category_col = self.extract_col.extract(self.target_attr, self.target_attr['明細ID'], extract_col=product_attr_tg_list)
# 元DSからカテゴリ列を除去することによって、非カテゴリ列を抽出
product_attr_non_category_col = self.extract_col.exclude(self.target_attr, exclude_col=product_attr_tg_list)
# 特徴量抽出
org_product_attr = self.encode.transform_feature(product_attr_category_col, aggregate_col=product_attr_tg_list)
org_product_attr = org_product_attr.fillna(0)
#org_product_attr = org_product_attr.drop('Unnamed: 0', axis=1)
#print(org_product_attr)
# ラベル付与
for col in product_attr_tg_list:
product_attr_col_list += self.encode.transform_label(self.target_attr[col],col)
else:
product_attr_col_list += ['明細ID']
# ラベル設定
org_product_attr.columns = product_attr_col_list
# カテゴリ列と非カテゴリ列を結合
feat_product_attr = pd.merge(org_product_attr, product_attr_non_category_col, on='明細ID',how='left')
feat_product_attr = feat_product_attr.drop('Unnamed: 0', axis=1)
'''
# product_attr
feat_product_attr = self.average_attr
# --- check ---
#print("--- feat_product_attr shape ---\n {}\n".format(feat_cust_attr.shape))
#print(feat_product_attr.head())
#self.file_io.export_csv_from_pandas(feat_product_attr, './data/out/mid_feat_product_attr.csv')
# cust
cust_col_list = []
cust_tg_list = ['性別', '携帯TEL', '自宅TEL', '携帯メール', 'PCメール', '職業']
# 外れ値を削除
new_cust = self.cust.drop(self.cust[self.cust['生年月日'].str.contains(
'\*', na=True)].index)
today = int(pd.to_datetime('today').strftime('%Y%m%d'))
new_cust['生年月日'] = pd.to_datetime(
new_cust['生年月日']).dt.strftime('%Y%m%d').astype(np.int64)
new_cust['生年月日'] = ((today - new_cust['生年月日']) / 10000).astype(
np.int64)
new_cust['生年月日'] = self.bin.list_divide(new_cust['生年月日'],
[0, 10, 20, 30, 40, 50],
['10', '20', '30', '40', '50'])
# カテゴリ列を抽出
cust_category_col = self.extract_col.extract(new_cust,
new_cust['顧客ID'],
extract_col=cust_tg_list)
# 非カテゴリ列を抽出
cust_non_category_col = self.extract_col.exclude(
new_cust, exclude_col=cust_tg_list)
# 特徴量抽出
feat_cust = self.encode.transform_feature(cust_category_col,
aggregate_col=cust_tg_list)
feat_cust = feat_cust.fillna(0)
#feat_cust = feat_cust.drop('Unnamed: 0', axis=1)
feat_cust = feat_cust[feat_cust.columns.drop(
list(feat_cust.filter(regex='Unnamed:')))]
# ラベル付与
for col in cust_tg_list:
cust_col_list += self.encode.transform_label(new_cust[col], col)
else:
cust_col_list += ['顧客ID']
# ラベル設定
feat_cust.columns = cust_col_list
# カテゴリ列と非カテゴリ列を結合
feat_cust = pd.merge(feat_cust,
cust_non_category_col,
on='顧客ID',
how='left')
#feat_cust = feat_cust.drop('Unnamed: 0', axis=1)
# --- check ---
#print("--- feat_cust shape ---\n {}\n".format(feat_cust.shape))
#print(feat_cust.head())
#self.file_io.export_csv_from_pandas(feat_cust, './data/out/mid_feat_cust.csv')
# shop
shop_col_list = []
shop_tg_list = ['担当店舗']
# カテゴリ列を抽出
shop_category_col = self.extract_col.extract(self.shop,
self.shop['顧客ID'],
extract_col=shop_tg_list)
# 特徴量抽出
feat_shop = self.encode.transform_feature(shop_category_col,
aggregate_col=shop_tg_list)
feat_shop = feat_shop.fillna(0)
#feat_shop = feat_cust.drop('Unnamed: 0', axis=1)
feat_shop = feat_shop[feat_shop.columns.drop(
list(feat_shop.filter(regex='Unnamed:')))]
# ラベル付与
for col in shop_tg_list:
shop_col_list += self.encode.transform_label(self.shop[col], col)
else:
shop_col_list += ['顧客ID']
# ラベル設定
feat_shop.columns = shop_col_list
#feat_shop = feat_shop.drop('Unnamed: 0', axis=1)
# --- check ---
#print("--- feat_shop shape ---\n {}\n".format(feat_shop.shape))
#print(feat_shop.head())
#self.file_io.export_csv_from_pandas(feat_shop, './data/out/mid_feat_shop.csv')
# pref
pref_col_list = []
pref_tg_list = ['町域']
new_pref = self.pref.drop(self.pref[self.pref['町域'] == 0].index)
# カテゴリ列を抽出
pref_category_col = self.extract_col.extract(new_pref,
new_pref['顧客ID'],
extract_col=pref_tg_list)
# 特徴量抽出
feat_pref = self.encode.transform_feature(pref_category_col,
aggregate_col=pref_tg_list)
feat_pref = feat_pref.fillna(0)
#feat_pref = feat_cust.drop('Unnamed: 0', axis=1)
feat_pref = feat_pref[feat_pref.columns.drop(
list(feat_pref.filter(regex='Unnamed:')))]
# ラベル付与
for col in pref_tg_list:
pref_col_list += self.encode.transform_label(self.pref[col], col)
else:
pref_col_list += ['顧客ID']
# ラベル設定
feat_pref.columns = pref_col_list
#feat_pref = feat_pref.drop('Unnamed: 0', axis=1)
# --- check ---
#print("--- feat_pref shape ---\n {}\n".format(feat_pref.shape))
#print(feat_pref.head())
#self.file_io.export_csv_from_pandas(feat_pref, './data/out/mid_feat_pref.csv')
# cancel
# カテゴリーデータなし
# --- check ---
#print("--- cancel shape ---\n {}\n".format(cancel.shape))
#print(cancel.head())
# contact
# カテゴリーデータなし
# --- check ---
#print("--- contact shape ---\n {}\n".format(contact.shape))
#print(contact.head())
# cti
# カテゴリーデータなし
# --- check ---
#print("--- cti shape ---\n {}\n".format(cti.shape))
#print(cti.head())
# stay_time
new_stay_time = self.stay_time
new_stay_time['滞在時間'] = self.bin.quant_divide(
new_stay_time['滞在時間'], 6, ['1', '2', '3', '4', '5'])
bin_stay_time = new_stay_time.drop('Unnamed: 0', axis=1)
# pv_sum
new_pv_sum = self.pv_sum
new_pv_sum['閲覧ページ総数'] = self.bin.quant_divide(
new_pv_sum['閲覧ページ総数'], 11,
['1', '2', '3', '4', '5', '6', '7', '8', '9', '10'])
bin_pv_sum = new_pv_sum.drop('Unnamed: 0', axis=1)
# session
new_session = self.session
new_session['閲覧ページ数/セッション'] = self.bin.quant_divide(
new_session['閲覧ページ数/セッション'], 6, ['1', '2', '3', '4', '5'])
bin_session = new_session.drop('Unnamed: 0', axis=1)
# register_type
reg_col_list = []
reg_tg_list = ['登録区分']
# カテゴリ列を抽出
reg_category_col = self.extract_col.extract(self.register_type,
self.register_type['顧客ID'],
extract_col=reg_tg_list)
# 非カテゴリ列を抽出
reg_non_category_col = self.extract_col.exclude(
self.register_type, exclude_col=reg_tg_list)
# 特徴量抽出
feat_register_type = self.encode.transform_feature(
reg_category_col, aggregate_col=reg_tg_list)
feat_register_type = feat_register_type.fillna(0)
#feat_register_type = feat_register_type.drop('Unnamed: 0', axis=1)
# ラベル付与
for col in reg_tg_list:
reg_col_list += self.encode.transform_label(
self.register_type[col], col)
else:
reg_col_list += ['顧客ID']
# ラベル設定
feat_register_type.columns = reg_col_list
# カテゴリ列と非カテゴリ列を結合
feat_register_type = pd.merge(feat_register_type,
reg_non_category_col,
on='顧客ID',
how='left')
feat_register_type = feat_register_type.drop('Unnamed: 0', axis=1)
# --- check ---
#print("--- feat_register_type shape ---\n {}\n".format(feat_register_type.shape))
#print(feat_register_type.head())
#self.file_io.export_csv_from_pandas(feat_register_type, './data/out/mid_feat_register_type.csv')
# status
stat_col_list = []
stat_tg_list = ['状況', '指名区分']
# カテゴリ列を抽出
stat_category_col = self.extract_col.extract(self.status,
self.status['顧客ID'],
extract_col=stat_tg_list)
# 非カテゴリ列を抽出
stat_non_category_col = self.extract_col.exclude(
self.status, exclude_col=stat_tg_list)
# 特徴量抽出
feat_status = self.encode.transform_feature(stat_category_col,
aggregate_col=stat_tg_list)
feat_status = feat_status.fillna(0)
#feat_status = feat_status.drop('Unnamed: 0', axis=1)
# ラベル付与
for col in stat_tg_list:
stat_col_list += self.encode.transform_label(self.status[col], col)
else:
stat_col_list += ['顧客ID']
# ラベル設定
feat_status.columns = stat_col_list
# カテゴリ列と非カテゴリ列を結合
feat_status = pd.merge(feat_status,
stat_non_category_col,
on='顧客ID',
how='left')
feat_status = feat_status.drop('Unnamed: 0', axis=1)
#feat_status = feat_status.drop('Unnamed: 0', axis=1)
# --- check ---
#print("--- feat_status shape ---\n {}\n".format(feat_status.shape))
#print(feat_status.head())
#self.file_io.export_csv_from_pandas(feat_status, './data/out/mid_feat_status.csv')
# 結合処理
con_file = pd.merge(feat_product_attr,
self.cust_payment,
on='顧客ID',
how='left')
#print("1.1: shape is {}".format(con_file.shape))
con_file = pd.merge(con_file, self.cancel, on='顧客ID', how='left')
#print("1.2: shape is {}".format(con_file.shape))
con_file = pd.merge(con_file, self.contact, on='顧客ID', how='left')
#print("1.3: shape is {}".format(con_file.shape))
con_file = pd.merge(con_file, self.cti, on='顧客ID', how='left')
#print("1.4: shape is {}".format(con_file.shape))
con_file = pd.merge(con_file, bin_stay_time, on='顧客ID', how='left')
#print("1.5: shape is {}".format(con_file.shape))
con_file = pd.merge(con_file, bin_pv_sum, on='顧客ID', how='left')
#print("1.6: shape is {}".format(con_file.shape))
con_file = pd.merge(con_file, bin_session, on='顧客ID', how='left')
#print("1.7: shape is {}".format(con_file.shape))
con_file = pd.merge(con_file, feat_cust_attr, on='顧客ID', how='left')
#print("1.8: shape is {}".format(con_file.shape))
con_file = pd.merge(con_file, feat_cust, on='顧客ID', how='left')
#print("1.9: shape is {}".format(con_file.shape))
con_file = pd.merge(con_file,
feat_register_type,
on='顧客ID',
how='left')
#print("1.10: shape is {}".format(con_file.shape))
#con_file = pd.merge(con_file, feat_status, on='顧客ID',how='left')
#print("1.11: shape is {}".format(con_file.shape))
'''con_file = pd.concat([
self.cust_payment,
feat_cust_attr,
feat_cust,
self.cancel,
self.contact,
self.cti,
feat_register_type,
feat_status,
self.stay_time,
self.pv_sum,
self.session], axis=1, join_axes=['顧客ID'])'''
# --- check ---
#print("--- con_file shape ---\n {}\n".format(con_file.shape))
#print(con_file.head())
# 結合処理
con_product_file = pd.merge(self.id,
self.cust_payment,
on='顧客ID',
how='left')
con_product_file = pd.merge(con_product_file,
feat_product_attr,
on='顧客ID',
how='left')
#print("2.1: shape is {}".format(con_file.shape))
# 重複がある場合、削除
con_file = con_file.drop_duplicates()
con_product_file = con_product_file.drop_duplicates()
con_product_file = con_product_file.drop(['施術時間', '売上単価', '数量'],
axis=1)
# 書き出し処理
self.file_io.export_csv_from_pandas(con_file, out_path)
self.file_io.export_csv_from_pandas(con_product_file, out_path2)
self.file_io.export_csv_from_pandas(feat_shop,
'./data/out/feat_shop.csv')
self.file_io.export_csv_from_pandas(feat_pref,
'./data/out/feat_pref.csv')