def clean_data(data: DataFrame):
columns = ['form_field1', 'form_field2', 'form_field3', 'form_field4', 'form_field5', 'form_field6', 'form_field7', 'form_field8', 'form_field9', 'form_field10', 'form_field12', 'form_field13', 'form_field14', 'form_field16', 'form_field17', 'form_field18', 'form_field19', 'form_field20', 'form_field21', 'form_field22', 'form_field24', 'form_field25', 'form_field26', 'form_field27', 'form_field28', 'form_field29', 'form_field32', 'form_field33', 'form_field34', 'form_field36', 'form_field37', 'form_field38', 'form_field39', 'form_field42', 'form_field43', 'form_field44', 'form_field46', 'form_field47', 'form_field48', 'form_field49', 'form_field50']
categories = [array(['charge', 'lending'], dtype=object)]
df = data[columns]
enc = OrdinalEncoder()
enc.categories_ = categories
df.form_field47 = enc.transform(df.form_field47.to_frame())
return df
def ordenc_to_sklearn(op_mapping):
"Converts OrdinalEncoder mapping to scikit-learn OrdinalEncoder."
cats = []
for column_map in op_mapping:
col = column_map['col']
while len(cats) <= col:
cats.append(None)
mapping = column_map['mapping']
res = []
for i in range(mapping.shape[0]):
if np.isnan(mapping.index[i]):
continue
ind = mapping.iloc[i]
while len(res) <= ind:
res.append(0)
res[ind] = mapping.index[i]
cats[col] = np.array(res, dtype=np.int64)
skl_ord = SklOrdinalEncoder(categories=cats, dtype=np.int64)
skl_ord.categories_ = cats
return skl_ord
def analisarPendente():
print(request.data)
K.clear_session()
data = json.loads(request.data)
model = load_model('SmartGardenModelBinary.h5')
dfTest = json_normalize(data)
le_insumo = LabelEncoder()
le_mes = OrdinalEncoder(categories=[[
'jan', 'fev', 'mar', 'abr', 'mai', 'jun', 'jul', 'ago', 'set', 'out',
'nov', 'dez'
]])
le_aparencia = OrdinalEncoder(
categories=[['murcha', 'amarelada', 'normal']])
le_temp = OrdinalEncoder(categories=[['baixo', 'medio', 'alto']])
le_umi = OrdinalEncoder(categories=[['baixo', 'medio', 'alto']])
le_risco = OrdinalEncoder(categories=[['baixo', 'medio', 'alto']])
le_insumo.classes_ = np.load('./pickle/insumo.npy', allow_pickle=True)
le_mes.categories_ = np.load('./pickle/mes.npy', allow_pickle=True)
le_aparencia.categories_ = np.load('./pickle/aparencia.npy',
allow_pickle=True)
le_temp.categories_ = np.load('./pickle/temp.npy', allow_pickle=True)
le_umi.categories_ = np.load('./pickle/umi.npy', allow_pickle=True)
le_risco.categories_ = np.load('./pickle/risco.npy', allow_pickle=True)
insumo = le_insumo.transform(dfTest['insumo'])
mes = le_mes.transform(dfTest[['mes']])
aparencia = le_aparencia.transform(dfTest[['aparencia']])
dfTest['insumo'] = insumo
dfTest['mes'] = mes
dfTest['aparencia'] = aparencia
predictions = model.predict(dfTest.values)
predictions[predictions >= 0.5] = 1
predictions[predictions < 0.5] = 0
nn_preds = pd.DataFrame(predictions, columns=[0, 1, 2, 0, 1, 2, 0, 1, 2])
nn_preds['encGeral'] = (nn_preds.iloc[:, 0:3] == 1).idxmax(1)
nn_preds['encUmi'] = (nn_preds.iloc[:, 3:6] == 1).idxmax(1)
nn_preds['encTemp'] = (nn_preds.iloc[:, 6:9] == 1).idxmax(1)
nn_preds['stRisco'] = le_risco.inverse_transform(nn_preds[['encGeral']])
nn_preds['stUmi'] = le_umi.inverse_transform(nn_preds[['encUmi']])
nn_preds['stTemp'] = le_temp.inverse_transform(nn_preds[['encTemp']])
nn_preds = nn_preds[['stRisco', 'stUmi', 'stTemp']]
nn_preds = nn_preds.to_json()
nn_preds = json.loads(nn_preds)
nn_preds['stRisco'] = nn_preds['stRisco']['0']
nn_preds['stTemp'] = nn_preds['stTemp']['0']
nn_preds['stUmi'] = nn_preds['stUmi']['0']
return json.dumps(nn_preds)
