def post(self, domain):
body = tornado.escape.json_decode(self.request.body)
app_id = body["app_id"]
api = DatabaseAPI()
krequest = kintoneRequest(env=api.get_kintone_env(domain))
dfe = krequest.request_to_dfe(body)
self.train(self, app_id, krequest, dfe)
def post(self, domain):
body = tornado.escape.json_decode(self.request.body)
app_id = body["app_id"]
api = DatabaseAPI()
krequest = kintoneRequest(env=api.get_kintone_env(domain))
dfe = krequest.download(body)
_df = dfe.df.copy(deep=True)
predictor = Predictor.load_from_env(krequest.env, app_id)
pred = predictor.predict(dfe.df)
pred_column = pred.name + "_prediction"
_df[pred_column] = pred
type_header = {}
for c in _df.columns:
if c in dfe.df.columns.tolist() + [pred.name, pred_column]:
_c = c if c != pred_column else pred.name
type_header[c] = FTypeNames[dfe.ftypes[_c]]
else:
type_header[c] = ""
if c == pred.name:
type_header[c] += "/TGT" # todo: control attirubte not in api
elif c == pred_column:
type_header[c] += "/PRED"
type_header[c] = [type_header[c]] # for from_dict columns
dfe = None # free memory
ordered = [
c for c in _df.columns if c not in [pred.name, pred_column]
] + [pred.name, pred_column]
_df = _df[ordered]
self.download_file(_df, "prediction.tsv", type_header)
def test_file_to_dfe(self):
krequest = kintoneRequest()
dfe = krequest.file_to_df(FILE_STR.strip().encode("utf-8"))
self.assertEqual(dfe.target, "家賃")
self.assertEqual(dfe.get_columns(FType.categorical), ["向き"])
self.assertEqual(dfe.get_columns(FType.numerical),
["築年数(年)", "専有面積(mxm)", "家賃"])
def post(self, domain):
app_id = self.get_argument("app_id", None, True)
error = None
if not app_id or not app_id.isdigit():
error = ErrorMessage.create("アプリケーションIDが正しく指定されていません")
api = DatabaseAPI()
krequest = kintoneRequest(env=api.get_kintone_env(domain))
dfe = krequest.file_to_df(self.request.body)
TrainingHandler.train(self, app_id, krequest, dfe)
def post(self, domain):
body = tornado.escape.json_decode(self.request.body)
app_id = body["app_id"]
values = body["values"]
api = DatabaseAPI()
krequest = kintoneRequest(env=api.get_kintone_env(domain))
df = krequest.record_to_df(body)
predictor = Predictor.load_from_env(krequest.env, app_id)
pred = predictor.predict(df).values
result = {"prediction": "{}".format(pred[0])}
self.write(result)
def test_autorun_from_request(self):
print("AutoRun from Request --------------")
request = json.loads(KINTONE_REQUEST.replace("###", str(self.APP_ID)))
krequest = kintoneRequest()
dfe = krequest.request_to_dfe(request)
dfe.to_categorical(["向き"])
autorun = make_autorun(dfe, feature_type_estimation=False)
descriptions = autorun.execute()
for d in descriptions:
print(d)
print("Predict from kintone Request --------------")
record = json.loads(KINTONE_RECORD.replace("###", str(self.APP_ID)))
df = krequest.record_to_df(record)
predictor = autorun.to_predictor()
pred = predictor.predict(df)
print("estimated value: {}".format(pred))
def test_request_to_dfe(self):
request = json.loads(KINTONE_REQUEST.replace("###", str(self.APP_ID)))
krequest = kintoneRequest()
dfe = krequest.request_to_dfe(request)
print(dfe.ftypes)
print(dfe.df.shape)