init

stock/util/StockPredictor.py

@@ -0,0 +1,59 @@
+import os
+import keras
+import numpy as np
+from keras.applications.imagenet_utils import decode_predictions
+from classification_models.keras import Classifiers
+
+class StockPredictor:
+
+    RESOURCE_PATH = None
+    stock2Vector = None
+
+    def __init__(self):
+        return
+
+    def getDataset(self, df):
+        VECTOR_SIZE = 299
+        TOTAL_X, TOTAL_Y = [], []
+        for key in df:
+            if key == "date":
+                continue
+            elif key == "label":
+                TOTAL_Y.append(df[key].tolist())
+            else:
+                TOTAL_X.append(df[key].tolist())
+
+        SIZE_WIDTH = len(TOTAL_X[0])
+        SIZE_HEIGHT = len(TOTAL_X)
+        X = []
+        for i in range(VECTOR_SIZE, SIZE_WIDTH):
+            temp_X, temp_Y = np.zeros((VECTOR_SIZE, VECTOR_SIZE)), np.zeros(0)
+            for j in range(SIZE_HEIGHT):
+                temp_X[j][0:VECTOR_SIZE] = TOTAL_X[j][i - VECTOR_SIZE:i]
+            temp_X = np.stack([temp_X, temp_X, temp_X], axis=-1)
+            X.append(temp_X)
+
+        X = np.asarray(X[len(X)-1])
+
+        return X
+
+    def predict(self, df, minmax_df, isRealTime=False):
+        X = self.getDataset(df)
+
+        # build model
+        n_classes = 3
+        Inceptionresnetv2, preprocess_input = Classifiers.get('inceptionresnetv2')
+        X = preprocess_input(X)
+        base_model = Inceptionresnetv2(input_shape=(299, 299, 3), include_top=False)
+        model = keras.models.Model(inputs=[base_model.input])
+
+        checkpoint_filename = os.path.join(self.RESOURCE_PATH, "model", "stock.ckpt")
+        model.load_weights(checkpoint_filename)
+
+        y = model.predict(X)
+
+        # result
+        print(decode_predictions(y))
+
+
+        return