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generate_dataset.py

@@ -18,7 +18,8 @@ from huggingface_hub import HfApi
 
 # load original datasets
 stock_price = array(load_dataset("nateraw/airbnb-stock-price")["train"])
-stock_price_2 = array(load_dataset("nateraw/airbnb-stock-price-new-new-new")["train"])
+stock_price_2 = array(load_dataset("nateraw/airbnb-stock-price-2")["train"])
+datasets = (stock_price, stock_price_2)
 
 def convert_to_timestamp(date: str) -> float:
     "Converts different date format strings from the datasets into a timestamp."
@@ -40,28 +41,17 @@ def data_generator():
 
     dates = [] # to not have duplicate dates
 
-    for price in stock_price:
-        row = {}
-        row["date"] = convert_to_timestamp(price["Date"])
-        dates.append(row["date"])
-        row["open"] = price["Open"]
-        row["close_last"] = price["Adj.Close"]
-        row["volume"] = price["Volume"]
-        row["high"] = price["High"]
-        row["low"] = price["Low"]
-        yield row
-
-    for price in stock_price_2:
-        row = {}
-        row["date"] = convert_to_timestamp(price["date"])
-        if row["date"] in dates:
-            continue # prevent having duplicate dates
-        row["open"] = price["open"]
-        row["close_last"] = price["close_last"]
-        row["volume"] = price["volume"]
-        row["high"] = price["high"]
-        row["low"] = price["low"]
-        yield row
+    for stock in datasets:
+        for price in stock:
+            row = {}
+            row["date"] = convert_to_timestamp(price["Date"] if "Date" in price else price["open"])
+            dates.append(row["date"])
+            row["open"] = price["Open"] if "Open" in price else price["open"]
+            row["close_last"] = price["Adj.Close"] if "Adj.Close" in price else price["open"]
+            row["volume"] = price["Volume"] if "Volume" in price else price["open"]
+            row["high"] = price["High"] if "High" in price else price["open"]
+            row["low"] = price["Low"] if "Low" in price else price["open"]
+            yield row
 
 # generate dataset object, export them, and push to hub
 dataset = Dataset.from_generator(data_generator)