Create xgb_training.py

xgb_training.py

@@ -0,0 +1,42 @@
+# developer: Taoshidev
+# Copyright © 2023 Taoshi, LLC
+
+# developer: Taoshidev
+# Copyright © 2023 Taoshi, LLC
+
+import random
+
+import numpy as np
+from sklearn.preprocessing import MinMaxScaler
+
+from mining_objects.xgb_mining_model import BaseMiningModel
+from mining_objects.mining_utils import MiningUtils
+from time_util.time_util import TimeUtil
+from vali_objects.dataclasses.client_request import ClientRequest
+from vali_config import ValiConfig
+
+import bittensor as bt
+
+# historical doesnt have timestamps
+data_structure = MiningUtils.get_file("/runnable/historical_financial_data/data.pickle", True)
+        #data_structure = [data_structure[0][curr_iter:curr_iter+iter_add],
+         #                     data_structure[1][curr_iter:curr_iter+iter_add],
+          #                    data_structure[2][curr_iter:curr_iter+iter_add],
+           #                   data_structure[3][curr_iter:curr_iter+iter_add],
+            #                  data_structure[4][curr_iter:curr_iter+iter_add]]
+print(len(data_structure[0]))
+print("start", TimeUtil.millis_to_timestamp(data_structure[0][0]))
+print("end", TimeUtil.millis_to_timestamp(data_structure[0][len(data_structure[0])-1]))
+sds_ndarray = np.array(data_structure).T
+
+scaler = MinMaxScaler(feature_range=(0, 1))
+
+scaled_data = scaler.fit_transform(sds_ndarray)
+scaled_data = scaled_data.T
+
+
+
+# will iterate and prepare the dataset and train the model as provided
+prep_dataset = BaseMiningModel.base_model_dataset(scaled_data)
+base_mining_model = BaseMiningModel(len(prep_dataset.T)).set_model_dir('./mining_models/xgbTrain.model')
+base_mining_model.train(prep_dataset)#, epochs=25)