docstr/pylint of GP Tuner & CurveFitting Assessor

src/sdk/pynni/nni/curvefitting_assessor/curvefitting_assessor.py

@@ -29,13 +29,13 @@ class CurvefittingAssessor(Assessor):
 
     Parameters
     ----------
-    epoch_num: int
+    epoch_num : int
         The total number of epoch
-    optimize_mode: str
+    optimize_mode : str
         optimize mode, 'maximize' or 'minimize'
-    start_step: int
+    start_step : int
         only after receiving start_step number of reported intermediate results
-    threshold: float
+    threshold : float
         The threshold that we decide to early stop the worse performance curve.
     """
     def __init__(self, epoch_num=20, optimize_mode='maximize', start_step=6, threshold=0.95, gap=1):
@@ -70,9 +70,9 @@ def trial_end(self, trial_job_id, success):
 
         Parameters
         ----------
-        trial_job_id: int
+        trial_job_id : int
             trial job id
-        success: bool
+        success : bool
             True if succssfully finish the experiment, False otherwise
         """
         if success:
@@ -90,9 +90,9 @@ def assess_trial(self, trial_job_id, trial_history):
 
         Parameters
         ----------
-        trial_job_id: int
+        trial_job_id : int
             trial job id
-        trial_history: list
+        trial_history : list
             The history performance matrix of each trial
 
         Returns
@@ -105,7 +105,6 @@ def assess_trial(self, trial_job_id, trial_history):
         Exception
             unrecognize exception in curvefitting_assessor
         """
-        trial_job_id = trial_job_id
         self.trial_history = trial_history
         if not self.set_best_performance:
             return AssessResult.Good

src/sdk/pynni/nni/curvefitting_assessor/curvefunctions.py

@@ -14,7 +14,9 @@
 # NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
 # DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
-
+"""
+A family of functions used by CurvefittingAssessor
+"""
 import numpy as np
 
 all_models = {}
@@ -29,10 +31,10 @@ def vap(x, a, b, c):
 
     Parameters
     ----------
-    x: int
-    a: float
-    b: float
-    c: float
+    x : int
+    a : float
+    b : float
+    c : float
 
     Returns
     -------
@@ -50,10 +52,10 @@ def pow3(x, c, a, alpha):
 
     Parameters
     ----------
-    x: int
-    c: float
-    a: float
-    alpha: float
+    x : int
+    c : float
+    a : float
+    alpha : float
 
     Returns
     -------
@@ -71,9 +73,9 @@ def linear(x, a, b):
 
     Parameters
     ----------
-    x: int
-    a: float
-    b: float
+    x : int
+    a : float
+    b : float
 
     Returns
     -------
@@ -91,9 +93,9 @@ def logx_linear(x, a, b):
 
     Parameters
     ----------
-    x: int
-    a: float
-    b: float
+    x : int
+    a : float
+    b : float
 
     Returns
     -------
@@ -112,10 +114,10 @@ def dr_hill_zero_background(x, theta, eta, kappa):
 
     Parameters
     ----------
-    x: int
-    theta: float
-    eta: float
-    kappa: float
+    x : int
+    theta : float
+    eta : float
+    kappa : float
 
     Returns
     -------
@@ -133,10 +135,10 @@ def log_power(x, a, b, c):
 
     Parameters
     ----------
-    x: int
-    a: float
-    b: float
-    c: float
+    x : int
+    a : float
+    b : float
+    c : float
 
     Returns
     -------
@@ -154,11 +156,11 @@ def pow4(x, alpha, a, b, c):
 
     Parameters
     ----------
-    x: int
-    alpha: float
-    a: float
-    b: float
-    c: float
+    x : int
+    alpha : float
+    a : float
+    b : float
+    c : float
 
     Returns
     -------
@@ -177,11 +179,11 @@ def mmf(x, alpha, beta, kappa, delta):
 
     Parameters
     ----------
-    x: int
-    alpha: float
-    beta: float
-    kappa: float
-    delta: float
+    x : int
+    alpha : float
+    beta : float
+    kappa : float
+    delta : float
 
     Returns
     -------
@@ -199,11 +201,11 @@ def exp4(x, c, a, b, alpha):
 
     Parameters
     ----------
-    x: int
-    c: float
-    a: float
-    b: float
-    alpha: float
+    x : int
+    c : float
+    a : float
+    b : float
+    alpha : float
 
     Returns
     -------
@@ -221,9 +223,9 @@ def ilog2(x, c, a):
 
     Parameters
     ----------
-    x: int
-    c: float
-    a: float
+    x : int
+    c : float
+    a : float
 
     Returns
     -------
@@ -242,11 +244,11 @@ def weibull(x, alpha, beta, kappa, delta):
 
     Parameters
     ----------
-    x: int
-    alpha: float
-    beta: float
-    kappa: float
-    delta: float
+    x : int
+    alpha : float
+    beta : float
+    kappa : float
+    delta : float
 
     Returns
     -------
@@ -264,11 +266,11 @@ def janoschek(x, a, beta, k, delta):
 
     Parameters
     ----------
-    x: int
-    a: float
-    beta: float
-    k: float
-    delta: float
+    x : int
+    a : float
+    beta : float
+    k : float
+    delta : float
 
     Returns
     -------

src/sdk/pynni/nni/curvefitting_assessor/model_factory.py

@@ -40,7 +40,7 @@ class CurveModel:
 
     Parameters
     ----------
-    target_pos: int
+    target_pos : int
         The point we need to predict
     """
     def __init__(self, target_pos):
@@ -120,14 +120,14 @@ def predict_y(self, model, pos):
 
         Parameters
         ----------
-        model: string
+        model : string
             name of the curve function model
-        pos: int
+        pos : int
             the epoch number of the position you want to predict
 
         Returns
         -------
-        int:
+        int
             The expected matrix at pos
         """
         if model_para_num[model] == 2:
@@ -143,9 +143,9 @@ def f_comb(self, pos, sample):
 
         Parameters
         ----------
-        pos: int
+        pos : int
             the epoch number of the position you want to predict
-        sample: list
+        sample : list
             sample is a (1 * NUM_OF_FUNCTIONS) matrix, representing{w1, w2, ... wk}
 
         Returns
@@ -165,7 +165,7 @@ def normalize_weight(self, samples):
 
         Parameters
         ----------
-        samples: list
+        samples : list
             a collection of sample, it's a (NUM_OF_INSTANCE * NUM_OF_FUNCTIONS) matrix,
             representing{{w11, w12, ..., w1k}, {w21, w22, ... w2k}, ...{wk1, wk2,..., wkk}}
 
@@ -187,7 +187,7 @@ def sigma_sq(self, sample):
 
         Parameters
         ----------
-        sample: list
+        sample : list
             sample is a (1 * NUM_OF_FUNCTIONS) matrix, representing{w1, w2, ... wk}
 
         Returns
@@ -206,9 +206,9 @@ def normal_distribution(self, pos, sample):
 
         Parameters
         ----------
-        pos: int
+        pos : int
             the epoch number of the position you want to predict
-        sample: list
+        sample : list
             sample is a (1 * NUM_OF_FUNCTIONS) matrix, representing{w1, w2, ... wk}
 
         Returns
@@ -225,7 +225,7 @@ def likelihood(self, samples):
 
         Parameters
         ----------
-        sample: list
+        sample : list
             sample is a (1 * NUM_OF_FUNCTIONS) matrix, representing{w1, w2, ... wk}
 
         Returns
@@ -244,7 +244,7 @@ def prior(self, samples):
 
         Parameters
         ----------
-        samples: list
+        samples : list
             a collection of sample, it's a (NUM_OF_INSTANCE * NUM_OF_FUNCTIONS) matrix,
             representing{{w11, w12, ..., w1k}, {w21, w22, ... w2k}, ...{wk1, wk2,..., wkk}}
 
@@ -267,7 +267,7 @@ def target_distribution(self, samples):
 
         Parameters
         ----------
-        samples: list
+        samples : list
             a collection of sample, it's a (NUM_OF_INSTANCE * NUM_OF_FUNCTIONS) matrix,
             representing{{w11, w12, ..., w1k}, {w21, w22, ... w2k}, ...{wk1, wk2,..., wkk}}
 
@@ -322,7 +322,7 @@ def predict(self, trial_history):
 
         Parameters
         ----------
-        trial_history: list
+        trial_history : list
             The history performance matrix of each trial.
 
         Returns