feat: rewrite ML utilities to Cython

Makefile

@@ -0,0 +1,2 @@
+cutils:
+	cd restful/cutils && python setup.py build_ext --inplace && cd ../..

restful/cutils/setup.py

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+from setuptools import setup
+from Cython.Build import cythonize
+import numpy
+
+setup(
+    ext_modules=cythonize("utilities.pyx"),
+    include_dirs=[numpy.get_include()]
+)
+

restful/cutils/utilities.pyx

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+import os
+from joblib import load
+from numpy import append, expand_dims
+from pandas import read_json, to_datetime, Timedelta
+from tensorflow.keras.models import load_model
+import cython
+
+cdef class Utilities:
+    async def cryptocurrency_prediction_utils(self,
+        int days, int sequence_length, str model_name) -> tuple:
+        cdef str model_path = os.path.join('./models', f'{model_name}.keras')
+        model = load_model(model_path)
+
+        cdef str dataframe_path = os.path.join('./posttrained', f'{model_name}-posttrained.json')
+        dataframe = read_json(dataframe_path)
+        dataframe.set_index('Date', inplace=True)
+
+        minmax_scaler = load(os.path.join('./pickles', f'{model_name}_minmax_scaler.pickle'))
+        standard_scaler = load(os.path.join('./pickles', f'{model_name}_standard_scaler.pickle'))
+        
+        # Prediction
+        lst_seq = dataframe[-sequence_length:].values
+        lst_seq = expand_dims(lst_seq, axis=0)
+
+        cdef dict predicted_prices = {}
+        last_date = to_datetime(dataframe.index[-1])
+
+        for _ in range(days):
+            predicted_price = model.predict(lst_seq)
+            last_date = last_date + Timedelta(days=1)
+
+            predicted_prices[last_date] = minmax_scaler.inverse_transform(predicted_price)
+            predicted_prices[last_date] = standard_scaler.inverse_transform(predicted_prices[last_date])
+
+            lst_seq = append(lst_seq[:, 1:, :], [predicted_price], axis=1)
+
+        predictions = [
+            {'date': date.strftime('%Y-%m-%d'), 'price': float(price)} \
+                for date, price in predicted_prices.items()
+        ]
+
+        # Actual
+        df_date = dataframe.index[-sequence_length:].values
+        df_date = [to_datetime(date) for date in df_date]
+
+        dataframe[['Close']] = minmax_scaler.inverse_transform(dataframe)
+        dataframe[['Close']] = standard_scaler.inverse_transform(dataframe)
+        df_close = dataframe.iloc[-sequence_length:]['Close'].values
+
+        actuals = [
+            {'date': date.strftime('%Y-%m-%d'), 'price': close} \
+                for date, close in zip(df_date, df_close)
+        ]
+        
+        return actuals, predictions
+

restful/services.py

@@ -1,4 +1,4 @@
-from restful.utilities import Utilities
+from restful.cutils.utilities import Utilities
 from restful.schemas import CryptocurrencyPredictionSchema
 
 class cryptocurrency_svc: