update curvefit (log, p0, maxfev)

Signed-off-by: Sunyanan Choochotkaew <[email protected]>

Author: sunya-ch

src/train/trainer/ExponentialRegressionTrainer/main.py

@@ -6,6 +6,13 @@
 from trainer.curvefit import CurveFitTrainer, CurveFitModel
 
 import numpy as np
+import math
+
+def p0_func(x, y):
+    a = (y.max()-y.min())//math.e # scale value
+    b = 1 # start from linear
+    c = y.min() - a # initial offset
+    return [a,b,c]
 
 def expo_func(x, a, b, c):
     y = a*np.exp(b*x) + c 
@@ -18,4 +25,4 @@ def __init__(self, energy_components, feature_group, energy_source, node_level,
         self.fe_files = []
 
     def init_model(self):
-        return CurveFitModel(expo_func)
+        return CurveFitModel(expo_func, p0_func=p0_func)

src/train/trainer/LogarithmicRegressionTrainer/main.py

@@ -8,13 +8,13 @@
 import numpy as np
 
 def p0_func(x, y):
-    print(y.max(), y.min())
     a = y.max()-y.min()
-    b = y.min()
-    return [a, b]
+    b = 1
+    c = y.min()
+    return [a, b, c]
 
-def log_func(x, a, b):
-    y = [a * np.log(xi) + b if xi > 0 else 0 for xi in x]
+def log_func(x, a, b, c):
+    y = a*np.log(b*x+1) + c
     return y
 
 class LogarithmicRegressionTrainer(CurveFitTrainer):

src/train/trainer/LogisticRegressionTrainer/main.py

@@ -10,7 +10,7 @@
 def p0_func(x, y):
     A = y.max() - y.min() # value range
     x0 = 0.5 # sigmoid mid point (as normalized value is in 0 to 1, start mid point = 0.5)
-    k = A/np.std(y) # growth rate (larger std, lower growth)
+    k = A//np.std(y) # growth rate (larger std, lower growth)
     off = y.min() # initial offset
     return [A,x0,k,off]
 

src/train/trainer/curvefit.py

@@ -36,9 +36,9 @@ def fit(self, X_values, y_values):
         flatten_x = self._x_values(X_values)
         flatten_y = np.array(y_values).flatten()
         if self.p0_func is not None:
-            self.popt, self.pcov = curve_fit(self.fit_func, flatten_x, flatten_y, p0=self.p0_func(flatten_x, flatten_y), maxfev=20000)
+            self.popt, self.pcov = curve_fit(self.fit_func, flatten_x, flatten_y, p0=self.p0_func(flatten_x, flatten_y), maxfev=30000)
         else:
-            self.popt, self.pcov = curve_fit(self.fit_func, flatten_x, flatten_y, maxfev=20000)
+            self.popt, self.pcov = curve_fit(self.fit_func, flatten_x, flatten_y, maxfev=30000)
 
     def predict(self, X_values):
         if self.popt is None: