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Commit fec9c13c authored by Bogdan Mihai (ARTICONF student)'s avatar Bogdan Mihai (ARTICONF student)
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Deleted obsolete DB files

parent e1216230
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Showing with 97 additions and 735 deletions
src/participation-hub/federated-learning-microservice/app/configs/routes.yml
View file @ fec9c13c
swagger: '2.0'
paths:
#####
# USE-CASES
#####
/use-cases:
post:
security:
- JwtRegular: []
operationId: "routes.use_case.add"
tags:
- "Use-Cases"
summary: "Adds a new Use-Case"
description: "Adds a new Use-Case"
parameters:
- in: body
name: "Object"
required: true
schema:
type: object
properties:
name:
type: string
example: "use_case_1"
description: "Unique name for the Use-Case"
responses:
'200':
description: "Successful Request"
'400':
description: "Use-Case already exists"
get:
security:
- JwtRegular: []
operationId: "routes.use_case.all"
tags:
- "Use-Cases"
summary: "Retrieves all Use-Cases"
description: "Retrieves all Use-Cases"
responses:
'200':
description: "Successful Request"
delete:
security:
- JwtAdmin: []
operationId: "routes.use_case.delete_all"
tags:
- "Use-Cases"
summary: "Delete all Use-Cases"
description: "Delete all Use-Cases"
responses:
'200':
description: "Successful Request"
#####
# Owners
#####
/Owners/use_case/{use_case}/last_train:
......@@ -228,12 +178,12 @@ paths:
definitions:
Data_entry:
type: "object"
required:
- internal
properties:
internal:
type: string
example: "todo, create the preprocessing part"
\ No newline at end of file
# definitions:
# Data_entry:
# type: "object"
# required:
# - internal
# properties:
# internal:
# type: string
# example: "todo, create the preprocessing part"
\ No newline at end of file
src/participation-hub/federated-learning-microservice/app/db/entities/layer_adapter.py deleted 100644 → 0
View file @ e1216230
from typing import Dict, List
class LayerAdapter:
'''
represents the mapping from an internal layer with a set of attributes and what
attributes from the dataset correspond to each one
'''
def __init__(self, name: str, use_case: str, table: str, properties: List[str], cluster_properties: List[str]):
'''
Creates a new instance of LayerAdapter
@params:
name - Required : unique identifier for the layer
properties - Required : list of mappings from the Schema
cluster_properties - Required : subset of properties to indicate what properties are used for clustering
use_case - Required : identifier for the use-case this layer belongs to
'''
self.name = name
self.properties = properties
self.use_case = use_case
self.table = table
for prop in cluster_properties:
if prop not in properties:
raise ValueError(f"{prop} is no property in the layer!")
self.cluster_properties = cluster_properties
def add_mapping(self, internal: str):
'''
Add a new mapping to the layer. This mapping consists of an internal representation.
@params:
internal - Required: string identifier used internally, f.e. "startTime"
external - Required: string identifier the column has in the external dataset f.e. "arrivalTimeOfCustomer"
'''
if internal not in self.properties:
self.properties.append(internal)
def delete_mapping(self, internal: str):
'''
Removes a proprety from the layer. Raises a ValueError if the property identified by the internal representation
does not exist.
@params:
internal - Required: string identifier used internally, f.e. "startTime"
'''
if internal not in self.properties:
raise ValueError(f"Attribute {internal} is not an internal attribute!")
if internal in self.cluster_properties:
self.delete_cluster_mapping(internal)
self.properties.remove(internal)
def add_cluster_mapping(self, attribute:str):
'''
Adds an attribute of the internal representations to the set of attributes to cluster after. The set of cluster
attribute is always a subset of self.properties.keys().
@params:
attribute - Required: string identifier used internally, f.e. "startTime"
'''
if attribute not in self.properties:
raise ValueError(f"Attribute {attribute} is not an internal attribute!")
if attribute not in self.cluster_properties:
self.cluster_properties.append(attribute)
def delete_cluster_mapping(self, attribute:str):
'''
Removes an attribute from the set of cluster-attributes. Raises a ValueError if either the attribute does not exist
as an internal representation
@params:
attribute - Required: string identifier used internally, f.e. "startTime"
'''
if attribute not in self.properties:
raise ValueError(f"Attribute {attribute} is not an internal attribute!")
self.cluster_properties.remove(attribute)
def to_serializable_dict(self) -> Dict:
return {
"name": self.name,
"properties": self.properties,
"cluster_properties": self.cluster_properties,
"use_case": self.use_case,
"table": self.table
}
@staticmethod
def from_serializable_dict(user_dict: Dict):
'''
creates a layer object from a dictionary. has to have the following keys:
- name
- properties
- cluster_properties
- use_case
'''
return LayerAdapter(
user_dict["name"],
user_dict["use_case"],
user_dict["table"],
user_dict["properties"],
user_dict["cluster_properties"]
)
src/participation-hub/federated-learning-microservice/app/db/entities/table.py deleted 100644 → 0
View file @ e1216230
from typing import Dict
class Table:
def __init__(self, use_case: str, name: str, mappings: Dict[str, str]):
self.use_case = use_case
self.name = name
self.mappings = mappings
def to_serializable_dict(self) -> Dict:
return {
"name": self.name,
"use_case": self.use_case,
"mappings": self.mappings
}
def add_mapping(self, internal:str, external:str):
if internal not in self.mappings.keys():
self.mappings[internal] = external
@staticmethod
def from_serializable_dict(data: Dict):
return Table(
data["use_case"],
data["name"],
data["mappings"]
)
src/participation-hub/federated-learning-microservice/app/db/entities/use_case.py deleted 100644 → 0
View file @ e1216230
from typing import Dict, List
class UseCase:
'''
represents the mapping from an internal layer with a set of attributes and what
attributes from the dataset correspond to each one
'''
def __init__(self, name: str):
'''
Creates a new instance of LayerAdapter
@params:
name - Required : unique identifier for the layer
properties - Required : maps InternalPropertyName->DatasetPropertyName
cluster_properties - Required : subset of the keys of properties, marks properties to cluster with
use_case - Required : identifier for the use-case this layer belongs to
'''
self.name = name
def to_serializable_dict(self) -> Dict:
return {
"name": self.name,
}
@staticmethod
def from_serializable_dict(user_dict: Dict):
'''
creates a layer object from a dictionary. has to have the following keys:
- name
- properties
- cluster_properties
- use_case
'''
return UseCase(
user_dict["name"],
)
src/participation-hub/federated-learning-microservice/app/db/repository.py deleted 100644 → 0
View file @ e1216230
# global imports (dont't worry, red is normal)
import network_constants as netconst
from database.MongoRepositoryBase import MongoRepositoryBase
from db.entities.layer_adapter import LayerAdapter
from db.entities.use_case import UseCase
import pymongo
import json
from typing import List, Dict
class Repository(MongoRepositoryBase):
'''This is a repository for MongoDb.'''
def __init__(self):
super().__init__(netconst.BUSINESS_LOGIC_DB_HOSTNAME,
netconst.BUSINESS_LOGIC_DB_PORT,
'business-logic-db')
self._adapter_collection = 'layer_adapters'
self._use_case_collection = 'use_cases'
def all(self) -> List[Dict]:
result = super().get_entries(self._adapter_collection, projection={'_id': False})
return [LayerAdapter.from_serializable_dict(row) for row in list(result)]
def all_for_use_case(self, use_case: str) -> List[LayerAdapter]:
result = super().get_entries(self._adapter_collection, projection={'_id': False}, selection={"use_case": use_case})
return [LayerAdapter.from_serializable_dict(row) for row in list(result)]
def all_for_use_case_and_table(self, use_case: str, table: str) -> List[LayerAdapter]:
result = super().get_entries(self._adapter_collection, projection={'_id': False}, selection={"use_case": use_case, "table": table})
return [LayerAdapter.from_serializable_dict(row) for row in list(result)]
def one(self, name : str, use_case: str, table: str) -> LayerAdapter:
result = list(super().get_entries(self._adapter_collection, selection={"name": name, "use_case": use_case, "table": table}))
if len(result) == 1:
return LayerAdapter.from_serializable_dict(result[0])
return None
def delete_all(self):
collection = self._database[self._adapter_collection]
collection.delete_many({})
def add(self, adapter : LayerAdapter):
super().insert_entry(self._adapter_collection, adapter.to_serializable_dict())
def update_use_case(self, adapter : LayerAdapter, use_case: str):
collection = self._database[self._adapter_collection]
collection.update_one({"name":adapter.name, "use_case": use_case, "table": adapter.table}, {"$set": adapter.to_serializable_dict()})
def update(self, adapter : LayerAdapter):
collection = self._database[self._adapter_collection]
collection.update_one({"name":adapter.name, "use_case": adapter.use_case, "table": adapter.table}, {"$set": adapter.to_serializable_dict()})
def delete(self, adapter : LayerAdapter):
collection = self._database[self._adapter_collection]
collection.delete_many({"name": adapter.name, "use_case": adapter.use_case, "table": adapter.table})
\ No newline at end of file
src/participation-hub/federated-learning-microservice/app/db/table_repository.py deleted 100644 → 0
View file @ e1216230
# global imports (dont't worry, red is normal)
import network_constants as netconst
from database.MongoRepositoryBase import MongoRepositoryBase
from db.entities.table import Table
from typing import Dict, List
class TableRepository(MongoRepositoryBase):
def __init__(self):
super().__init__(netconst.BUSINESS_LOGIC_DB_HOSTNAME,
netconst.BUSINESS_LOGIC_DB_PORT,
'business-logic-db')
self._collection = 'tables'
def get_all(self) -> List[Table]:
result = super().get_entries(self._collection, projection={'_id': False})
return [Table.from_serializable_dict(row) for row in list(result)]
def get_all_for_use_case(self, use_case:str) -> List[Table]:
result = super().get_entries(self._collection, selection={'use_case': use_case}, projection={'_id': False})
return [Table.from_serializable_dict(row) for row in result]
def get_for_use_case_and_name(self, use_case:str, name:str) -> Table:
result = list(super().get_entries(self._collection, selection={"use_case":use_case, "name":name}, projection={'_id': False}))
if len(result) == 1:
return Table.from_serializable_dict(result[0])
if len(result) == 0:
return None
raise ValueError("No more than 1 Schema allowed per use-case!")
def add(self, table:Table):
super().insert_entry(self._collection, table.to_serializable_dict())
def delete_for_use_case(self, use_case: str):
collection = self._database[self._collection]
collection.delete_many({"use_case": use_case})
def delete_all(self):
collection = self._database[self._collection]
collection.delete_many({})
def update(self, table: Table):
collection = self._database[self._collection]
collection.update_one({"use_case": table.use_case, "name": table.name}, {"$set": table.to_serializable_dict()})
\ No newline at end of file
src/participation-hub/federated-learning-microservice/app/db/use_case_repository.py deleted 100644 → 0
View file @ e1216230
# global imports (dont't worry, red is normal)
import network_constants as netconst
from database.MongoRepositoryBase import MongoRepositoryBase
from db.entities.use_case import UseCase
import pymongo
import json
from typing import List, Dict
class UseCaseRepository(MongoRepositoryBase):
'''This is a repository for MongoDb.'''
def __init__(self):
super().__init__(netconst.BUSINESS_LOGIC_DB_HOSTNAME,
netconst.BUSINESS_LOGIC_DB_PORT,
'business-logic-db')
self._use_case_collection = 'use_cases'
def all(self) -> List[UseCase]:
dicts = list(super().get_entries(self._use_case_collection, projection={'_id': False}))
return [UseCase.from_serializable_dict(d) for d in dicts]
def delete_all(self):
collection = self._database[self._use_case_collection]
collection.delete_many({})
def delete_all_with_name(self, name: str):
collection = self._database[self._use_case_collection]
collection.delete_many({"name": name})
def get_by_name(self, name: str):
result = list(super().get_entries(self._use_case_collection, {"name":name}))
if len(result) == 1:
return UseCase.from_serializable_dict(result[0])
if len(result) == 0:
return None
raise ValueError("More than one Use-Case in the DB!")
def put(self, use_case_name: str):
use_cases = self.all()
existing_use_cases = list(filter(lambda use_case: use_case.name == use_case_name, use_cases))
if len(existing_use_cases) == 0:
use_case = UseCase(use_case_name)
super().insert_entry(self._use_case_collection, use_case.to_serializable_dict())
\ No newline at end of file
src/participation-hub/federated-learning-microservice/app/main.py
View file @ fec9c13c
......@@ -17,8 +17,6 @@ from flask import redirect
app = connexion.App(__name__, specification_dir='configs/')
from db.entities.layer_adapter import LayerAdapter
@app.app.before_request
def before_request():
if request.url.startswith('http://'):
......
src/participation-hub/federated-learning-microservice/app/processing/crowd_journalism/federated_algorithm.py 0 → 100644
View file @ fec9c13c
#import processing.text_processing.global_hyperparams as globals
import global_hyperparams as globals
from model import get_simple_LSTM_model
import pandas as pd
from sklearn.model_selection import train_test_split
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout, Embedding
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
import collections
import numpy as np
import tensorflow as tf
import tensorflow_federated as tff
def model_fn():
keras_model = get_simple_LSTM_model()
#return tff.learning.from_compiled_keras_model(keras_model, sample_batch) original
return tff.learning.from_keras_model(
keras_model,
input_spec=globals.INPUT_SPEC,
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])
def federated_computation_new(train_dataset,test_dataset):
if(globals.INPUT_SPEC == None):
#should never reach this place because INPUT_SPEC is instantiated inside get_preprocessed_train_test_data.
#however, if in the future, processed data is provided without hte preprocessing function it will be none -> therefore assign it here
globals.INPUT_SPEC = train_dataset[0].element_spec
# Training and evaluating the model
iterative_process = tff.learning.build_federated_averaging_process(model_fn,client_optimizer_fn=lambda: tf.keras.optimizers.SGD(lr=0.5))
state = iterative_process.initialize()
print(type(state))
for n in range(globals.EPOCHS):
state, metrics = iterative_process.next(state, train_dataset)
print('round {}, training metrics={}'.format(n+1, metrics))
evaluation = tff.learning.build_federated_evaluation(model_fn)
eval_metrics = evaluation(state.model, train_dataset)
print('Training evaluation metrics={}'.format(eval_metrics))
test_metrics = evaluation(state.model, test_dataset)
print('Test evaluation metrics={}'.format(test_metrics))
return state,metrics
###############################################################################################
def federated_computation_continue(train_dataset,test_dataset,restored_state):
if(globals.INPUT_SPEC == None):
#should never reach this place because INPUT_SPEC is instantiated inside get_preprocessed_train_test_data.
#however, if in the future, processed data is provided without hte preprocessing function it will be none -> therefore assign it here
globals.INPUT_SPEC = train_dataset[0].element_spec
# Training and evaluating the model
iterative_process = tff.learning.build_federated_averaging_process(model_fn,client_optimizer_fn=lambda: tf.keras.optimizers.SGD(lr=0.5))
state = iterative_process.initialize()
state = restored_state
print(type(state))
for n in range(globals.EPOCHS):
state, metrics = iterative_process.next(state, train_dataset)
print('round {}, training metrics={}'.format(n+1, metrics))
evaluation = tff.learning.build_federated_evaluation(model_fn)
eval_metrics = evaluation(state.model, train_dataset)
print('Training evaluation metrics={}'.format(eval_metrics))
test_metrics = evaluation(state.model, test_dataset)
print('Test evaluation metrics={}'.format(test_metrics))
return state,metrics
###############################################################################################
src/participation-hub/federated-learning-microservice/app/processing/text_processing/ledger.csv
View file @ fec9c13c
Trainer_id,Model_id,Dataset_id,Accuracy,Loss
0,1623766462,1623766462,0.5,nan
0,1623768325,1623768325,0.25,nan
1,1623768510,1623768510,0.75,nan
src/participation-hub/federated-learning-microservice/app/routes/developers.py
View file @ fec9c13c
......@@ -52,5 +52,5 @@ def trainMetricsToJSON(last_train_metrics : list):
metricsDict["Loss"] = last_train_metrics[4]
return json.dumps(metricsDict)
#upload_and_train("text_processing",1)
upload_and_train("text_processing",1)
src/participation-hub/federated-learning-microservice/app/routes/layer.py deleted 100644 → 0
View file @ e1216230
#global imports
from db.entities.layer_adapter import LayerAdapter
from db.repository import Repository
from db.table_repository import TableRepository
from db.use_case_repository import UseCaseRepository
from services.layer_adapter_service import LayerAdapterService
import json
from flask import Response, request
table_repository = TableRepository()
layer_repository = Repository()
use_case_repository = UseCaseRepository()
def all():
return [layer.to_serializable_dict() for layer in layer_repository.all()]
def delete_all_layers():
'''
delete all layers from the DB
'''
layer_repository.delete_all()
return Response(status=200)
def add_complete():
'''
add a layer already containing attribute mappings to the BD.
'''
data = request.json
if "name" not in data or "properties" not in data or "cluster_properties" not in data or "use_case" not in data or "table" not in data:
return Response(status=400, response=f"Field missing! Fields required: (name, properties, cluster_properties, table), present:({data.keys()})")
use_case = data["use_case"]
table_name = data["table"]
layer_name = data["name"]
table = table_repository.get_for_use_case_and_name(use_case, table_name)
if table == None:
return Response(status=400, response="Table does not exist!")
layer = layer_repository.one(layer_name, use_case, table.name)
if layer != None:
return Response(status=400, response=f'Layer with name "{data["name"]}" already exists!')
# check if table contains mapping
for prop in data["properties"]:
if prop not in table.mappings.keys():
return Response(status=400, response=f"'{prop}' is no property of the Table!")
try:
layer_new = LayerAdapter.from_serializable_dict(data)
except BaseException as e:
print(f"Exception: {e}")
return Response(status=400, response=f"{e}")
try:
LayerAdapterService.add_complete(layer_new)
except ValueError as e:
return Response(status = 400, response=f"{e}")
return Response(status=200)
def add_cluster_mapping(use_case: str, table: str, name: str):
'''
add a mapped property to the list of properties to cluster with
@params:
use_case - Required : String-identifier for the Use-Case the Layer belongs to
table - Required : unique identifier of the Table the Layer belongs to
name - Required : unique identifier for the Layer
'''
layer = layer_repository.one(name, use_case, table)
if layer == None:
return Response(status=404, response=f"Layer with name '{name}' does not exist!")
data = request.json
if "attribute" not in data:
return Response(status=400, response=f"Field missing! Fields required: (attribute)")
try:
layer.add_cluster_mapping(data["attribute"])
layer_repository.update(layer)
return Response(status=200)
except:
return Response(status=400, response=f'{data["attribute"]} is no attribute in the layer!')
def delete_cluster_mapping(use_case: str, table: str, name: str):
'''
remove a mapped property from the list of properties to cluster with
@params:
use_case - Required : String-identifier for the Use-Case the Layer belongs to
table - Required : unique identifier of the Table the Layer belongs to
name - Required : unique identifier for the Layer
'''
layer = layer_repository.one(name, use_case, table)
if layer == None:
return Response(status=404, response=f"Layer with name '{name}' does not exist!")
data = request.json
if "attribute" not in data:
return Response(status=400, response=f"Field missing! Fields required: (attribute)")
try:
layer.delete_cluster_mapping(data["attribute"])
layer_repository.update(layer)
return Response(status=200)
except ValueError as e:
print(e)
return Response(status=400, response=f'{data["attribute"]} is no attribute in the layer!')
def get_all_for_use_case(use_case: str):
'''
get all layers assigned to the given use_case
'''
use_case_repository.put(use_case)
return [layer.to_serializable_dict() for layer in layer_repository.all_for_use_case(use_case)]
def one(use_case: str, table: str, name: str):
'''
fetch a single layer from the DB
@params:
use_case - Required : String-identifier for the Use-Case the Layer belongs to
table - Required : unique identifier of the Table the Layer belongs to
name - Required : unique identifier for the Layer
'''
layer = layer_repository.one(name, use_case, table)
if layer == None:
return Response(status=404, response=f"Layer with name '{name}' does not exist!")
return Response(status=200, response=json.dumps(layer.to_serializable_dict()))
def delete_mapping(use_case: str, table: str, name: str):
'''
delete a mapping from the layer identified by the internal representation
@params:
use_case - Required : String-identifier for the Use-Case the Layer belongs to
table - Required : unique identifier of the Table the Layer belongs to
name - Required : unique identifier for the Layer
'''
use_case_repository.put(use_case)
layer = layer_repository.one(name, use_case, table)
if layer == None:
return Response(status=404, response=f"Layer with name '{name}' does not exist!")
data = request.json
if "internal" not in data:
return Response(status=400, response=f"Field missing! Fields required: (internal)")
try:
layer.delete_mapping(data["internal"])
layer_repository.update(layer)
except ValueError:
return Response(status=400, response=f'{data["internal"]} is not a property of the layer!')
return Response(status=200)
def add_mapping(name: str, table: str, use_case: str):
'''
add a new mapping to the layer identified by name
@params:
use_case - Required : String-identifier for the Use-Case the Layer belongs to
table - Required : unique identifier of the Table the Layer belongs to
name - Required : unique identifier for the Layer
'''
use_case_repository.put(use_case)
layer = layer_repository.one(name, use_case)
if layer == None:
return Response(status=404, response=f"Layer with name '{name}' does not exist!")
data = request.json
if "internal" not in data:
return Response(status=400, response=f"Field missing! Fields required: (internal)")
# check if schema contains mapping
table = table_repository.get_for_use_case_and_name(use_case, table)
if table == None:
return Response(status=400, response=f'Table does not exist.')
if data["internal"] not in table.mappings:
return Response(status=400, response=f'{data["internal"]} is not existent in the table!')
layer.add_mapping(data["internal"])
layer_repository.update(layer)
return Response(status=200)
def delete_one(use_case: str, table: str, name: str):
'''
delete a layer and all its mappings from the Db
@params:
use_case - Required : String-identifier for the Use-Case the Layer belongs to
table - Required : unique identifier of the Table the Layer belongs to
name - Required : unique identifier for the Layer
'''
layer = layer_repository.one(name, use_case, table)
if layer == None:
return Response(status=404, response=f"Layer with name '{name}' does not exist!")
layer_repository.delete(layer)
return Response(status=200)
def get_layers_for_use_case_and_table(use_case: str, table: str):
return [layer.to_serializable_dict() for layer in layer_repository.all_for_use_case_and_table(use_case, table)]
\ No newline at end of file
src/participation-hub/federated-learning-microservice/app/routes/owners.py
View file @ fec9c13c
......@@ -74,6 +74,7 @@ def trainMetricsToJSON(last_train_metrics : list):
metricsDict["Accuracy"] = last_train_metrics[3]
metricsDict["Loss"] = last_train_metrics[4]
return json.dumps(metricsDict)
#last("text_processing")
#upload_and_train("text_processing") #warning it deletes the files
\ No newline at end of file
upload_and_train("text_processing") #warning it deletes the files
last("text_processing")
\ No newline at end of file
src/participation-hub/federated-learning-microservice/app/routes/tables.py deleted 100644 → 0
View file @ e1216230
#global imports
from db.table_repository import TableRepository
from db.use_case_repository import UseCaseRepository
from db.entities.table import Table
from flask import Response, request
table_repository = TableRepository()
use_case_repository = UseCaseRepository()
def all():
return [t.to_serializable_dict() for t in table_repository.get_all()]
def all_for_use_case(use_case:str):
return [t.to_serializable_dict() for t in table_repository.get_all_for_use_case(use_case)]
def put_mapping(use_case:str, name:str):
body = request.json
if "internal" not in body or "external" not in body:
return Response(status=400, response=f"Field missing! Fields required: (internal, external)")
table = table_repository.get_for_use_case_and_name(use_case, name)
if table == None:
print("table not there, creating it...")
table = table_repository.add(Table(use_case, name, {}))
table.add_mapping(body["internal"], body["external"])
table_repository.update(table)
return Response(status=200)
def add_complete(use_case: str):
body = request.json
body["use_case"] = use_case
# check if fields are present
if "name" not in body or "mappings" not in body:
return Response(status=400, response="Field missing! Fields required: (name, mappings)")
# check if table exists
table_reference = table_repository.get_for_use_case_and_name(body["use_case"], body["name"])
if table_reference != None:
return Response(status=400, response="Table already exists!")
use_case_repository.put(body["use_case"])
table_new = Table.from_serializable_dict(body)
table_repository.add(table_new)
return Response(status=200)
def delete_all_for_use_case(use_case: str):
table_repository.delete_for_use_case(use_case)
return Response(status=200)
def delete_all():
table_repository.delete_all()
return Response(status=200)
\ No newline at end of file
src/participation-hub/federated-learning-microservice/app/routes/use_case.py deleted 100644 → 0
View file @ e1216230
# global imports
from db.entities.layer_adapter import LayerAdapter
from db.use_case_repository import UseCaseRepository
from db.table_repository import TableRepository
from db.repository import Repository
from services.layer_adapter_service import LayerAdapterService
import json
from flask import Response, request
use_case_repository = UseCaseRepository()
table_repository = TableRepository()
repository = Repository()
def all():
return [use_case.to_serializable_dict() for use_case in use_case_repository.all()]
def delete_all():
use_case_repository.delete_all()
repository.delete_all()
table_repository.delete_all()
return Response(status=200)
def add():
body = request.json
if "name" not in body.keys():
return Response(status=400, response="Field missing! Fields required: (name)")
name = body["name"]
# check if use-case exists
reference = use_case_repository.get_by_name(name)
if not reference == None:
return Response(status=400, response="Use-Case already exists!")
use_case_repository.put(name)
return Response(status=200)
src/participation-hub/federated-learning-microservice/app/tests/test_layer_adapter.py deleted 100644 → 0
View file @ e1216230
import unittest
import manage_sys_paths
from db.entities.layer_adapter import LayerAdapter
class Test_Layer_Adapter(unittest.TestCase):
def test_LayerAdapter_newLayerAdapterObj_validInstantiation(self):
adapter1 = LayerAdapter("layer1", "use_case", "table", ["a", "c"], ["a"])
print(adapter1.to_serializable_dict)
if __name__ == '__main__':
unittest.main()
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