From 0ac64a7719e5d03aa20473900e7255b89926515c Mon Sep 17 00:00:00 2001
From: dmt <>
Date: Mon, 28 Oct 2019 21:37:18 +0100
Subject: [PATCH] Suppress warnings called by sklearn.
---
cml/ports/ml_adapter.py | 16 +++++++++++++++-
1 file changed, 15 insertions(+), 1 deletion(-)
diff --git a/cml/ports/ml_adapter.py b/cml/ports/ml_adapter.py
index fcd5212..2f9f4dc 100644
--- a/cml/ports/ml_adapter.py
+++ b/cml/ports/ml_adapter.py
@@ -1,5 +1,6 @@
from collections import Counter
from abc import abstractmethod
+import warnings
from numpy import array, linspace, less, greater, std, argsort
from scipy.signal import argrelextrema
@@ -21,8 +22,14 @@ SCIKIT_CLUSTERING_TABLE = {
}
-class MachineLearningModel:
+def log_warning(func):
+ def wrapper(*args, **kwargs):
+ warnings.filterwarnings("ignore")
+ return func(*args, **kwargs)
+ return wrapper
+
+class MachineLearningModel:
@abstractmethod
def train(self, data, *args, **kwargs):
pass
@@ -32,6 +39,7 @@ class FilterMethod(MachineLearningModel):
def __init__(self, model):
self.__model = model
+ @log_warning
def train(self, data, *args, **kwargs):
self.__model = self.__model.fit(data)
return self
@@ -48,6 +56,7 @@ class EmbeddedMethod(MachineLearningModel):
def __init__(self, model):
self.__model = model
+ @log_warning
def train(self, data, *args, **kwargs):
labels = data.get_column_values("Z")
self.__model = self.__model.fit(data, labels)
@@ -91,6 +100,7 @@ class ConstructionClusteringMLModel(MachineLearningModel):
)
return Counter(labels)
+ @log_warning
def train(self, data, *args, **kwargs):
self.__model.fit(data)
return self
@@ -103,6 +113,7 @@ class ReconstructionConceptualMLModel(MachineLearningModel):
self.accuracy = None
self.subject = model.__class__.__name__
+ @log_warning
def train(self, data, *args, **kwargs):
# TODO (dmt): Improve signature of this function!
labels = args[0]
@@ -121,6 +132,7 @@ class ReconstructionProceduralMLModel(MachineLearningModel):
self.max_error = None
self.subject = model.__class__.__name__
+ @log_warning
def train(self, data, *args, **kwargs):
# TODO (dmt): Provide a better way dealing with
# zero values as max_abs_label!
@@ -149,6 +161,7 @@ class KernelDensityEstimator(MachineLearningModel):
self.bandwidth = bandwidth
self.gridsize = gridsize
+ @log_warning
def train(self, data, *args, **kwargs):
reshaped_data = array(data).reshape(-1, 1)
if not self.__model:
@@ -173,6 +186,7 @@ class Autoencoder(MachineLearningModel):
self.__model = None
self.__hidden_outputter = None
+ @log_warning
def train(self, data, *args, **kwargs):
inputer = Input(shape=(self.io_shape, ))
hidden = Dense(units=self.target_number,
--
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