change config file

k models/exchange/config.py

@@ -1,3 +1,6 @@
+import pandas as pd
+import numpy as np
+
 """
 Configuration file for univariate time series model.
 """
@@ -12,23 +15,51 @@
 # Configuration dictionary to create DataContainer Object
 data_proc_config = {
     # Method to remove non-stationarity of time series
-        # By default, create stationary time series using first 
-        # differencing.
+    # By default, create stationary time series using first
+    # differencing.
     "method": "diff",  # {str}
-        # Avaiable method: 
-        # "diff": remove non-stationarity by differncing.
+    # Avaiable method:
+    # "diff": remove non-stationarity by differncing.
     "diff.lag": 1,  # {int > 0}
-        # Lag of differencing,
-        # Notice: If set to zero, the 0 series will be returned (x[i] - x[i - 0] == 0)
+    # Lag of differencing,
+    # Notice: If set to zero, the 0 series will be returned (x[i] - x[i - 0] == 0)
     "diff.order": 1,  # {int >= 0}
-        # Order of differencing,
-        # Notice: 0 order differencing will return the original series.
-        # For k order differncing, the generated series will have length N - k
+    # Order of differencing,
+    # Notice: 0 order differencing will return the original series.
+    # For k order differncing, the generated series will have length N - k
     "test_ratio": 0.2,  # {0 <= float < 1}
-        # Ratio of test set to evaluate neural network.
-    "lag_for_sup": 3,  # {int > 0} 
-        # Total number of lag values used to generate
-        # supervised learning problem (SLP).
-        # Using m then the i-1 to i-m total m lagged variables 
-        # will be used to predict the i-index variable in the SLP.
+    # Ratio of test set to evaluate neural network.
+    "lag_for_sup": 3,  # {int > 0}
+    # Total number of lag values used to generate
+    # supervised learning problem (SLP).
+    # Using m then the i-1 to i-m total m lagged variables
+    # will be used to predict the i-index variable in the SLP.
+}
+
+
+# ================ Configuration for multivariate model.
+def load_multi_ex(file_dir: str) -> pd.DataFrame:
+    dataset = pd.read_csv(file_dir, delimiter="\t", index_col=0)
+    # Cleaning Data
+    dataset.dropna(how="any", axis=0, inplace=True)
+    dataset.replace(to_replace=".", value=np.NaN, inplace=True)
+    dataset.fillna(method="ffill", inplace=True)
+    dataset = dataset.astype(np.float32)
+    # DEXVZUS behaved abnomally
+    dataset.drop(columns=["DEXVZUS"], inplace=True)
+    return dataset
+
+
+CON_config = {
+    "max_lag": 3,
+    "train_ratio": 0.9,
+    "time_steps": 14
+}
+
+NN_config = {
+    "batch_size": 32,
+    "validation_split": 0.1,
+    "nn.lstm1": 256,
+    "nn.lstm2": 128,
+    "nn.dense1": 64
 }

k models/exchange/local_visualize.py

@@ -29,10 +29,24 @@
 from containers import *
 from methods import *
 from models import *
+from config import *
 
+file_dir = "./data/exchange_rates/exchange_rates_Daily.csv"
+
+container = MultivariateContainer(
+    file_dir,
+    "DEXCAUS",
+    load_multi_ex,
+    CON_config)
+
+# Create empty model
+model = MultivariateLSTM(container, NN_config, create_empty=True)
+
+load_target = "./saved_models/2018Sep11_14_01_1536688913/"
 
 model.load_model(
-    folder_dir="./saved_models/2018Sep11_14_01_1536688913/")
+    folder_dir=load_target
+)
 
 # Testing Data
 

k models/exchange/models.py

@@ -89,7 +89,11 @@ def fit_model(self):
 
 
 class MultivariateLSTM(BaseModel):
-    def __init__(self, container, config=None) -> None:
+    def __init__(
+        self, 
+        container, 
+        config: bool=None, 
+        create_empty: bool=False) -> None:
         """
         Initialization method.
         """
@@ -99,9 +103,11 @@ def __init__(self, container, config=None) -> None:
         \n\tFeature: {self.num_fea}")
 
         self.config = config
-
         self.container = container
-        self.core = self._construct_lstm(self.config)
+        if create_empty:
+            self.core = None
+        else:
+            self.core = self._construct_lstm(self.config)
         self._gen_file_name()
         print(
             f"\tMultivariateLSTM: Current model will be save to ./saved_models/f{MultivariateLSTM}/")

k models/exchange/multi_ex.py

@@ -28,36 +28,11 @@
 from containers import *
 from methods import *
 from models import *
+from config import *
 
 file_dir = "./data/exchange_rates/exchange_rates_Daily.csv"
 
-def load_multi_ex(file_dir: str) -> pd.DataFrame:
-    dataset = pd.read_csv(file_dir, delimiter="\t", index_col=0)
-    # Cleaning Data
-    dataset.dropna(how="any", axis=0, inplace=True)
-    dataset.replace(to_replace=".", value=np.NaN, inplace=True)
-    dataset.fillna(method="ffill", inplace=True)
-    dataset = dataset.astype(np.float32)
-    # DEXVZUS behaved abnomally
-    dataset.drop(columns=["DEXVZUS"], inplace=True)
-    return dataset
-
-
 # Setting up parameters.
-CON_config = {
-    "max_lag": 3,
-    "train_ratio": 0.9,
-    "time_steps": 14
-}
-
-NN_config = {
-    "batch_size": 32,
-    "validation_split": 0.1,
-    "nn.lstm1": 256,
-    "nn.lstm2": 128,
-    "nn.dense1": 64
-}
-
 container = MultivariateContainer(
     file_dir,
     "DEXCAUS",
@@ -66,4 +41,4 @@ def load_multi_ex(file_dir: str) -> pd.DataFrame:
 
 model = MultivariateLSTM(container, NN_config)
 model.fit_model(epochs=int(input("Training epochs >>> ")))
-model.save_model()
+model.save_model()