Changed linear regression model to reference data in tech dir vs local ref.

2026-09-07 11:21:14 +02:00 · 2020-12-02 15:20:50 -08:00
parent d111041385
commit dcd20a250a
4 changed files with 94 additions and 47 deletions
@@ -1,25 +1,64 @@
+# See LICENSE for licensing information.
+#
+# Copyright (c) 2016-2019 Regents of the University of California and The Board
+# of Regents for the Oklahoma Agricultural and Mechanical College
+# (acting for and on behalf of Oklahoma State University)
+# All rights reserved.
+#
+
 import os
 from sklearn.linear_model import LinearRegression
-import mapping
+from .analytical_util import *
+from globals import OPTS
+import debug

-reference_dir = "data"   
+relative_data_path = "/sim_data"
+data_filename = "data.csv"
+tech_path = os.environ.get('OPENRAM_TECH')
+data_dir = tech_path+'/'+OPTS.tech_name+relative_data_path

-def run_model(x,y,test_x,test_y):
-    mp = mapping.mapping()
-    model = LinearRegression()
-    model.fit(x, y)
-    print(model.coef_)
-    print(model.intercept_)
+class linear_regression():

-    pred = model.predict(test_x)
+    def get_prediction(self):

-    #print(pred)
-    unscaled_labels = mp.unscale_data(test_y.tolist(), reference_dir)
-    unscaled_preds = mp.unscale_data(pred.tolist(), reference_dir)
-    unscaled_labels, unscaled_preds = (list(t) for t in zip(*sorted(zip(unscaled_labels, unscaled_preds))))
-    avg_error = mp.abs_error(unscaled_labels, unscaled_preds)
-    max_error = mp.max_error(unscaled_labels, unscaled_preds)
-    min_error = mp.min_error(unscaled_labels, unscaled_preds)
+        train_sets = []
+        test_sets = []     
+               
+        file_path = data_dir +'/'+data_filename
+        num_points_train = 5

-    errors = {"avg_error": avg_error, "max_error":max_error, "min_error":min_error}    
-    return errors
+        non_ip_samples, unused_samples = sample_from_file(num_points_train, file_path, data_dir)
+        nip_features_subset, nip_labels_subset = non_ip_samples[:, :-1], non_ip_samples[:,-1:]
+        nip_test_feature_subset, nip_test_labels_subset = unused_samples[:, :-1], unused_samples[:,-1:]
+
+        train_sets = [(nip_features_subset, nip_labels_subset)]
+        test_sets = [(nip_test_feature_subset, nip_test_labels_subset)]
+
+        runs_per_model = 1
+
+        for train_tuple, test_tuple in zip(train_sets, test_sets):
+            train_x, train_y = train_tuple
+            test_x, test_y = test_tuple
+
+            errors = {}
+            min_train_set = None
+            for _ in range(runs_per_model):
+                new_error = self.run_model(train_x, train_y, test_x, test_y, data_dir)
+                debug.info(1, "Model Error: {}".format(new_error))
+
+    def run_model(x,y,test_x,test_y, reference_dir):
+        model = LinearRegression()
+        model.fit(x, y)
+
+        pred = model.predict(test_x)
+
+        #print(pred)
+        unscaled_labels = unscale_data(test_y.tolist(), reference_dir)
+        unscaled_preds = unscale_data(pred.tolist(), reference_dir)
+        unscaled_labels, unscaled_preds = (list(t) for t in zip(*sorted(zip(unscaled_labels, unscaled_preds))))
+        avg_error = abs_error(unscaled_labels, unscaled_preds)
+        max_error = max_error(unscaled_labels, unscaled_preds)
+        min_error = min_error(unscaled_labels, unscaled_preds)
+
+        errors = {"avg_error": avg_error, "max_error":max_error, "min_error":min_error}    
+        return errors