def delete_entity(db_name):
if db_name == "worker":
worker_id = user_input.get_id()
if not CheckInput.check_worker_existence(worker_id):
print("There is no such worker in db!\n")
else:
db.delete_worker(worker_id)
elif db_name == "journal":
journal_id = user_input.get_id()
if not CheckInput.check_journal_existence(journal_id):
print("There is no such journal in db!\n")
else:
db.delete_journal(journal_id)
elif db_name == "department":
department_id = user_input.get_id()
if not CheckInput.check_department_existence(department_id):
print("There is no such department in db!\n")
else:
db.delete_department(department_id)
elif db_name == "worker_card":
worker_card_id = user_input.get_id()
if not CheckInput.check_worker_card_existence(worker_card_id):
print("There is no such worker card in db!\n")
else:
db.delete_worker_card(worker_card_id)
def read_renter_list_entity_update_input(self):
renterListEntity = RentingListScheme()
renterListEntity.car_number = input("enter car number: ")
while not CheckInput.test_car_number(renterListEntity.car_number):
renterListEntity.car_number = input("Wrong input!\nTry again: ")
renterListEntity.date = input("enter renting date(format: year-mm-dd): ")
while not CheckInput.test_date(renterListEntity.date):
renterListEntity.date = input("Wrong input!\nTry again: ")
if CheckInput.check_renting_list_entity_existence(renterListEntity.car_number, renterListEntity.date):
renterListEntity.driver_license = input("enter renter driver license: ")
while not CheckInput.test_driver_license(renterListEntity.driver_license):
renterListEntity.driver_license = input("Wrong input!\nTry again: ")
while not CheckInput.check_renter_existence(renterListEntity.driver_license):
renter_entity = RenterScheme()
input_str = ""
input_str = input("There is no such renter!\nWanna add?\n(y/n): ")
for case in switch(input_str):
if case('y'):
renter_entity = self.read_renter_input()
renterListEntity.driver_license = renter_entity.driver_license
db.insert_renter(renter_entity)
break
if case('n'):
print("can't insert renting list entity")
return
else:
print("____________________")
print("no such option, try again!")
else:
print("There's no such renting list entity")
return None
return renterListEntity
def read_worker_card_update_input(self):
worker_card_entity = WorkerCardScheme()
worker_card_entity.id = input("enter id: ")
if CheckInput.check_worker_card_existence(worker_card_entity.id):
worker_card_entity.worker_id = input("enter worker_id: ")
while not CheckInput.check_worker_existence(
worker_card_entity.worker_id):
input_str = input(
"There is no such worker!\nWanna add?\n(y/n): ")
for case in switch(input_str):
if case('y'):
worker_entity = self.read_worker_input()
worker_card_entity.worker_id = worker_entity.id
db.insert_worker(worker_entity)
break
if case('n'):
print("can't insert worker card ")
return
else:
print("____________________")
print("no such option, try again!")
worker_card_entity.is_active = input("enter is active: ")
while worker_card_entity.is_active != "True" and worker_card_entity.is_active != "False":
worker_card_entity.is_active = input(
"Wrong input!\nTry again: ")
return worker_card_entity
else:
print("There is no such worker card in db!")
return None
def read_journal_update_input(self):
journal_entity = JournalScheme()
journal_entity.id = input("enter id: ")
if CheckInput.check_journal_existence(journal_entity.id):
print("exit timestamp:")
journal_entity.exit = self.get_date()
print("entrance timestamp:")
journal_entity.entrance = self.get_date()
journal_entity.worker_id = input("worker id: ")
while not CheckInput.check_worker_existence(
journal_entity.worker_id):
input_str = input(
"There is no such worker!\nWanna add?\n(y/n): ")
for case in switch(input_str):
if case('y'):
worker_entity = self.read_worker_input()
journal_entity.worker_id = worker_entity.id
db.insert_worker(worker_entity)
break
if case('n'):
print("can't insert journal")
return
else:
print("____________________")
print("no such option, try again!")
return journal_entity
else:
print("There is no such journal in db!")
return None
def read_worker_update_input(self):
workerEntity = WorkerScheme()
workerEntity.name = input("enter worker name: ")
workerEntity.id = input("enter worker id: ")
if CheckInput.check_worker_existence(workerEntity.id):
workerEntity.is_still_working = input(
"enter worker is still working (True/False): ")
while workerEntity.is_still_working != "True" and workerEntity.is_still_working != "False":
workerEntity.is_still_working = input(
"Wrong input!\nTry again: ")
workerEntity.department_id = input("enter department id: ")
while not CheckInput.check_department_existence(
workerEntity.department_id):
input_str = input(
"There is no such department!\nWanna add?\n(y/n): ")
for case in switch(input_str):
if case('y'):
department_entity = self.read_department_input()
workerEntity.department_id = department_entity.id
db.insert_department(department_entity)
break
if case('n'):
print("can't insert worker")
return
else:
print("____________________")
print("no such option, try again!")
return workerEntity
else:
print("There is no such worker in db!")
return None
def export_items_by_id(self, product_id: str, count: int = 1) -> None:
"""
Exports Items from the inventory by product id.
"""
CheckInput.check_type(product_id, str)
item = self.get_item_by_product_id(product_id)
self.export_items(item, count)
def delete_entity(db_name):
if db_name == "cars":
car_number = user_input.get_car_number_input()
if not CheckInput.check_car_existence(car_number):
print("There is no such car in db!\n")
else:
db.delete_car(car_number)
elif db_name == "renters":
driver_license = input("enter renter driver license: ")
while not CheckInput.test_driver_license(driver_license):
driver_license = input("Wrong input!\nTry again: ")
if not CheckInput.check_renter_existence(driver_license):
print("There is no such renter in db!\n")
else:
db.delete_renter(driver_license)
elif db_name == "renting_list":
car_number = user_input.get_car_number_input()
date = input("enter renting date(format: year-mm-dd): ")
while not CheckInput.test_date(date):
date = input("Wrong input!\nTry again: ")
if not CheckInput.check_renting_list_entity_existence(
car_number, date):
print("There is no such renting list entity in db!\n")
else:
db.delete_renting_list_entity(car_number, date)
def _check_input(self, size: Size, stuffing: Stuffing, fabric: Fabric,
has_glow: bool, name: str, description: str,
product_id: str) -> None:
CheckInput.check_type(size, Size)
CheckInput.check_type(stuffing, Stuffing)
CheckInput.check_type(fabric, Fabric)
CheckInput.check_type(has_glow, bool)
CheckInput.check_all_input_type([name, description, product_id], str)
def _check_input(self, colour: Colours, size: Size, stuffing: Stuffing,
fabric: Fabric, name: str, description: str,
product_id: str) -> None:
CheckInput.check_type(colour, Colours)
CheckInput.check_type(stuffing, Stuffing)
CheckInput.check_type(fabric, Fabric)
CheckInput.check_type(size, Size)
CheckInput.check_all_input_type([name, description, product_id], str)
def _check_input(self, sound_effects_number: int, colour: Colours,
has_batteries: bool, name: str, description: str,
product_id: str) -> None:
CheckInput.check_type(sound_effects_number, int)
CheckInput.check_type(colour, Colours)
CheckInput.check_type(has_batteries, bool)
CheckInput.check_all_input_type([name, description, product_id], str)
CheckInput.check_value_is_lower_equal_than_threshold(
sound_effects_number, 0)
def create_item(self, inventory_type: InventoryEnum, **product_details) -> Item:
"""
Constructs an item based on the inventory type.
"""
CheckInput.check_type(inventory_type, InventoryEnum)
if inventory_type == InventoryEnum.TOY:
return self.create_toy(**product_details)
if inventory_type == InventoryEnum.STUFFED_ANIMAL:
return self.create_stuffed_animal(**product_details)
return self.create_candy(**product_details)
def read_renter_update_input(self):
renterEntity = RenterScheme()
renterEntity.driver_license = input("enter renter driver license: ")
while not CheckInput.test_driver_license(renterEntity.driver_license):
renterEntity.driver_license = input("Wrong input!\nTry again: ")
if CheckInput.check_renter_existence(renterEntity.driver_license):
renterEntity.first_name = input("enter new renter first name: ")
renterEntity.last_name = input("enter new renter last name: ")
return renterEntity
else:
print("There is no such renter in db!")
return None
def _check_input(self, rooms_number: int, dimensions: float, min_age: int,
has_batteries: bool, name: str, description: str,
product_id: str) -> None:
"""
Checks input.
"""
CheckInput.check_all_input_type([rooms_number, min_age], int)
CheckInput.check_type(dimensions, int, float)
CheckInput.check_type(has_batteries, bool)
CheckInput.check_all_input_value_is_lower_equal_than_threshold(
[rooms_number, dimensions, min_age], 0)
CheckInput.check_all_input_type([description, product_id, name], str)
def _check_input(self, speed: float, jump_height: float,
is_grow_in_dark: bool, spider_type: SpiderType,
min_age: int, has_batteries: bool, name: str,
description: str, product_id: str) -> None:
"""
Checks input.
"""
CheckInput.check_all_input_type([speed, jump_height, min_age], int,
float)
CheckInput.check_type(spider_type, SpiderType)
CheckInput.check_all_input_type([is_grow_in_dark, has_batteries], bool)
CheckInput.check_all_input_type([name, description, product_id], str)
CheckInput.check_all_input_value_is_lower_equal_than_threshold(
[speed, jump_height, min_age], 0)
def NN_prediction(self, data, model_path=None):
"""
Predicting output with given data
:param data (df): input data
:param model_path (str): trained model directory. If none, run NN_model train and save the trained modle to the model path
:return: predicted result of the given data
"""
# check na data
data = CheckInput().check_na(data)
data = np.array(data).reshape((data.shape[0], data.shape[1]))
# load model
model_file = model_path + "/" + self.prefix + "-model.json"
model_weight_file = model_path + "/" + self.prefix + "-model.weight.h5"
logging.info("Loading model...")
model = None
try:
with open(model_file, 'r') as json_string:
logging.info("Loading model...")
model_json = json_string.read()
model = model_from_json(model_json)
json_string.close()
except Exception, e:
logging.error(
"Model file is not found! This model should be provided by %s",
model_path,
exc_info=True)
def _check_input_type(
order_number: int,
product_id: str,
name: str,
quantity: int,
item_type: InventoryEnum,
product_details: dict,
factory: FestiveSeasonFactory) -> None:
"""
Checks if all inputs are valid.
"""
CheckInput.check_all_input_type([product_id, name], str)
CheckInput.check_all_input_type([order_number, quantity], int)
CheckInput.check_type(item_type, InventoryEnum)
CheckInput.check_type(product_details, dict)
CheckInput.check_type(factory, FestiveSeasonFactory)
def read_renter_input(self):
renterEntity = RenterScheme()
renterEntity.first_name = input("enter renter first name: ")
renterEntity.last_name = input("enter renter last name: ")
renterEntity.driver_license = input("enter renter driver license: ")
while not CheckInput.test_driver_license(renterEntity.driver_license):
renterEntity.driver_license = input("Wrong input!\nTry again: ")
return renterEntity
def read_car_input(self):
carEntity = CarScheme()
carEntity.name = input("enter car name: ")
carEntity.color = input("enter car color: ")
carEntity.model = input("enter car model: ")
carEntity.car_number = input("enter car number: ")
while not CheckInput.test_car_number(carEntity.car_number):
carEntity.car_number = input("Wrong input!\nTry again: ")
carEntity.price_per_day = input("enter car's price per day: ")
while not CheckInput.test_price(carEntity.price_per_day):
carEntity.price_per_day = input("Wrong input!\nTry again: ")
carEntity.has_automatic_transmission = input("enter car if car has automatic transmision (True/False): ")
while carEntity.has_automatic_transmission != "True" and carEntity.has_automatic_transmission != "False":
carEntity.has_automatic_transmission = input("Wrong input!\nTry again: ")
return carEntity
def read_department_update_input(self):
department_entity = DepartmentScheme()
department_entity.id = input("enter id: ")
if CheckInput.check_department_existence(department_entity.id):
department_entity.production = input("enter production: ")
return department_entity
else:
print("There is no such department in db!")
return None
def read_car_update_input(self):
carEntity = CarScheme()
carEntity.car_number = input("enter car number: ")
while not CheckInput.test_car_number(carEntity.car_number):
carEntity.car_number = input("Wrong input!\nTry again: ")
if CheckInput.check_car_existence(carEntity.car_number):
carEntity.name = input("enter new car name: ")
carEntity.color = input("enter new car color: ")
carEntity.model = input("enter new car model: ")
carEntity.price_per_day = input("enter new car's price per day: ")
while not CheckInput.test_price(carEntity.price_per_day):
carEntity.price_per_day = input("Wrong input!\nTry again: ")
carEntity.has_automatic_transmission = input("enter if new car has automatic transmision (True/False): ")
while carEntity.has_automatic_transmission != "True" and carEntity.has_automatic_transmission != "False":
carEntity.has_automatic_transmission = input("Wrong input!\nTry again: ")
return carEntity
else:
print("There is no such car in db!")
return None
def MiniBatchKmeans(self, df, **kwargs):
"""
Mini-Batch Kmeans clustering, especially suitable for large data sets. See detailed arguments in sklearn.cluster.MiniBatchKmeans method
:param df (pd.Dataframe): input df
:param kwargs: parameters for mini-Batch-Kmeans clustering
kmin (int): minimum cluster numbers generated
kmax (int): maximum cluster numbres generated
max_iter (int): maximum iterations before stopping
batch_size (int): size of mini batches
verbose (bool): verbosity mode.
:return: df with labels
"""
kmin = kwargs.pop('kmin', 8)
kmax = kwargs.pop('kmax', 20)
df = CheckInput().check_na(df)
scaler = StandardScaler().fit(df)
df_scale = scaler.fit_transform(df)
# init dictionaries to hold inetia scores, labels, BIC scores
labels, bics, inertias = {}, {}, {}
for n_cluster in range(kmin, kmax):
mbkm = MiniBatchKMeans(n_clusters=n_cluster, **kwargs)
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=DeprecationWarning)
mbkm.fit(np.array(df_scale))
labels[n_cluster] = mbkm.labels_
cur_centers = mbkm.cluster_centers_
inertias[n_cluster] = mbkm.inertia_
# get index of data in each cluster
cluster_index = [[] for i in range(n_cluster)]
for i in range(
n_cluster
): # or np.sort(np.unique(labels[n_cluster])) if not start with 0
cluster_index[i] = np.arange(len(
labels[n_cluster]))[labels[n_cluster] == i]
bics[n_cluster] = XMeans().BIC(np.array(df_scale), cluster_index,
cur_centers)
# get the maximum bic value and corresponding labels
opt_n_cluster = max(bics, key=bics.get)
logging.info("Optimal cluster number has BIC value %f",
bics[opt_n_cluster])
opt_labels = labels[opt_n_cluster]
logging.info(
"The optimum clusters found is %d. And the inertia scores for each cluster count are %s",
int(opt_n_cluster), str(inertias))
df['labels'] = opt_labels
return df
def DBSCAN_clustering(self, df, eps=0.5, min_samps=8):
"""
:param df: input df for clustering rows
:param eps: maximum distance between 2 samples to be considered as in same cluster
:param min_samps: minimum number of neighbouring samples for a point to be considered as core point
:return: df with labels of each row
"""
# check input df
df = CheckInput().check_na(df)
scaler = StandardScaler().fit(df)
# scale along columns
df_scale = scaler.fit_transform(df)
# compute DBSCAN
db = DBSCAN(eps=eps, min_samples=min_samps,
algorithm='ball_tree').fit(df_scale)
# logging.info("Silhouette Coefficient: %s", str(silhouette_samples(df, db.labels_)))
df['label'] = db.labels_
return df
def check_if_item_enough(self, product_id: str, count: int) -> bool:
"""
Checks if the inventory has enough stock for exporting this item.
:return: True if it has enough items, False if not
"""
CheckInput.check_type(product_id, str)
CheckInput.check_type(count, int)
CheckInput.check_value_is_lower_equal_than_threshold(count, 0)
item = self.get_item_by_product_id(product_id)
if item is None:
return False
return self._items[item] >= count
def export_items(self, item: Item, count: int = 1) -> None:
"""
Exports Items from the inventory.
:param item: Item
:param count: the number of item, default is 1
"""
CheckInput.check_type(item, Item)
CheckInput.check_type(count, int)
CheckInput.check_value_is_lower_equal_than_threshold(count, 0)
if item not in self._items.keys():
raise KeyError(item, "Inventory doesn't have this item")
if self._items[item] < count:
raise ValueError("The Inventory doesn't have enough stock for %s" %
item)
self._items[item] -= count
def import_items(self, item: Item, count: int = 100) -> None:
"""
Add Items stock. If the item already existed in the inventory, increase the count.
Else put the item into inventory.
:param item: Item
:param count: the number of item, default is 1
"""
CheckInput.check_type(item, Item)
CheckInput.check_type(count, int)
CheckInput.check_value_is_lower_equal_than_threshold(count, 0)
if count < 100:
count = 100
if item in self._items.keys():
self._items[item] += count
return None
self._items[item] = count
def X_means(self, df, **kwargs):
"""
X-means algorithm for clustering input dataframe
:param df: input dataframe
:param kwargs: parameters for kmeans algorithm, including:
kmin (int): the minimum clusters classified, default: 2
kmax (int): maximum clusters classified, default: None
init (str or np.ndarray): ways to initialize first set of centers -
'kmeans++', 'random', or user-provided array of center coordinates
bic_cutoff (float): bic criterion for terminate center splitting, default: 1.0
max_iter (int): maximum iteration for kmeans cluster in specified region, default: 1000
kmeans_tole (float): center distance change criterion during kmeans clustering, default: 0.01
:return: df with labels
"""
df = CheckInput().check_na(df)
scaler = StandardScaler().fit(df)
df_scale = scaler.fit_transform(df)
# convert df to np.array
logging.info("Initializing X-means clustering!")
model = XMeans(**kwargs).fit(np.asarray(df_scale))
# logging.info("Silhouette Coefficient: %0.3s", str(silhouette_samples(df, model.labels)))
df['labels'] = model.labels
return df
def Kmeans(self, df, **kwargs):
# similar parrameter settings with previous minibatch kmeans
kmin = kwargs.pop('kmin', 8)
kmax = kwargs.pop('kmax', 20)
df = CheckInput().check_na(df)
scaler = StandardScaler().fit(df)
df_scale = scaler.fit_transform(df)
# init dictionaries to hold inetia scores, labels, BIC scores
labels, bics, inertias = {}, {}, {}
for n_cluster in range(kmin, kmax):
km = KMeans(n_clusters=n_cluster, **kwargs)
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=DeprecationWarning)
km.fit(np.array(df_scale))
labels[n_cluster] = km.labels_
cur_centers = km.cluster_centers_
inertias[n_cluster] = km.inertia_
# get index of data in each cluster
cluster_index = [[] for i in range(n_cluster)]
for i in range(
n_cluster
): # or np.sort(np.unique(labels[n_cluster])) if not start with 0
cluster_index[i] = np.arange(len(
labels[n_cluster]))[labels[n_cluster] == i]
bics[n_cluster] = XMeans().BIC(np.array(df_scale), cluster_index,
cur_centers)
# get the maximum bic value and corresponding labels
opt_n_cluster = max(bics, key=bics.get)
logging.info("Optimal cluster number has BIC value %f",
bics[opt_n_cluster])
# clustering on whole df
opt_labels = labels[opt_n_cluster]
logging.info(
"The optimum clusters found is %d. And the inertia scores for each cluster count are %s",
int(opt_n_cluster), str(inertias))
df['labels'] = opt_labels
return df
def get_date(self):
date = input("enter timestamp(format: %Y-%m-%d %H:%M:%S): ")
while not CheckInput.test_timestamp(date):
date = input("Wrong input!\nTry again: ")
return date
def _check_input(self, contains_nuts: bool, variety: ToffeeVariety,
has_lactose: bool, name: str, description: str,
product_id: str) -> None:
CheckInput.check_all_input_type([contains_nuts, has_lactose], bool)
CheckInput.check_type(variety, ToffeeVariety)
CheckInput.check_all_input_type([name, description, product_id], str)
def hierarchical_clustering(self,
df,
kmax=20,
method='ward',
dist='euclidean',
treeplot_dir='.',
show_plot=True):
"""
:param df (pd.dataframe): input df
:param kmax (int): max number of clusters to generate
:param method (str): algorithm to perform hierarchical clustering;
'ward', 'complete', 'average'
:param dist (str): distance method to compute the linkage;
'euclidean', 'l1', 'l2', 'manhatton', 'cosine', 'precomputed'
when method is 'ward', only 'euclidean' is accepted.
:param treeplot_dir (str): directory to get the hierarchical clustering tree plot.
PS: The name indicate the sample length
:param show_plot (bool): show cophenetic correlation coefficient and dendrogram plot or not
:return: df with labels
"""
if not os.path.exists(treeplot_dir):
os.makedirs(treeplot_dir)
df = CheckInput().check_na(df)
scaler = StandardScaler().fit(df)
df_scale = scaler.fit_transform(df)
hc_z = linkage(df_scale, method=method, metric=dist)
# compute the cophenetic correlation coefficient to check if the clustering preserve original distances
coph_coef, coph_dist = cophenet(hc_z, pdist(df_scale))
logging.info(
"Cophenetic correlation coefficient of this hierarchical clustering is %0.3f",
coph_coef)
# use elbow method to automatically determine the number of clusters
last_30 = hc_z[-kmax:, 2]
idx = np.arange(1, len(last_30) + 1)
# 2nd derivatives of the distances
acce = np.diff(last_30, 2)
# get the knee point: if the last iteration is the max value in acce, we want to 2 clusters
n_clusters = acce[::-1].argmax() + 2
logging.info("Clusters: %d", n_clusters)
# visualize the elbow method plot
file_symbol = len(df.columns)
out_elbow = treeplot_dir + "/" + "hc-elbow.%s.S%s.png" % (method,
file_symbol)
# plot the distance of last 30 clusters iteratively (inverse the order of distance)
if show_plot:
plt.title("Elbow method for cluster number selection")
plt.xlabel("Iteration")
plt.ylabel("Distance")
plt.plot(idx, last_30[::-1])
# the first and the last distance cannot compute 2nd derivatives
plt.plot(idx[1:-1], acce[::-1])
plt.tight_layout()
plt.savefig(out_elbow, dpi=200)
plt.close()
# get labels of each point
labels = fcluster(hc_z, n_clusters, criterion='maxclust')
# compute the silhoutte coefficient
# logging.info("Sihoutte coeffient of hierarchical clustering is %s", str(silhouette_samples(df, labels)))
df['label'] = labels
# visualize the dendrogram clustering tree for further check
out_dendro = treeplot_dir + "/" + "hc-dendrogram.%s.N%d.S%s.png" % (
method, n_clusters, file_symbol)
if show_plot:
plt.title("Hierarchical clustering dendrogram (lastp)")
plt.xlabel("Cluster size")
plt.ylabel("Distance")
dendrogram(
hc_z,
p=n_clusters,
truncate_mode='lastp',
show_leaf_counts=True,
leaf_rotation=90,
leaf_font_size=12,
show_contracted=True,
)
plt.tight_layout()
plt.savefig(out_dendro, dpi=200)
plt.close()
return df
