From ca246281c4b4df28cd6488d4b76433be167cc861 Mon Sep 17 00:00:00 2001
From: Nelly Barret <nelly.barret@etu.univ-lyon1.fr>
Date: Sat, 24 Oct 2020 11:52:11 +0200
Subject: [PATCH] [M] fixed remove rural IRIS
---
predihood/classes/Data.py | 3 ++-
predihood/classes/Dataset.py | 7 ++++---
predihood/predict.py | 8 ++++----
3 files changed, 10 insertions(+), 8 deletions(-)
diff --git a/predihood/classes/Data.py b/predihood/classes/Data.py
index b42b8923..4057c473 100644
--- a/predihood/classes/Data.py
+++ b/predihood/classes/Data.py
@@ -17,7 +17,7 @@ warnings.filterwarnings("ignore", category=RuntimeWarning)
class Data:
- def __init__(self, normalization="density", filtering=True):
+ def __init__(self, normalization="density", filtering=True, add_assessment=False):
"""
Constructor of the Data class. Initialize attributes.
Args:
@@ -31,6 +31,7 @@ class Data:
self.indicators = None
self.normalization = normalization
self.filtering = filtering
+ self.add_assessment = add_assessment
# retrieve indicators
self.get_indicators()
diff --git a/predihood/classes/Dataset.py b/predihood/classes/Dataset.py
index 9e3d24bd..366e019b 100644
--- a/predihood/classes/Dataset.py
+++ b/predihood/classes/Dataset.py
@@ -13,7 +13,7 @@ class Dataset:
"""
This class represents assessed IRIS with their indicators ans EV values. There are options, such as removing outliers or rural IRIS.
"""
- def __init__(self, data, env, _type, selected_indicators=None, indicators_to_remove=None, train_size=TRAIN_SIZE, test_size=TEST_SIZE, outliers=False):
+ def __init__(self, data, env, _type, selected_indicators=None, indicators_to_remove=None, train_size=TRAIN_SIZE, test_size=TEST_SIZE, outliers=False, remove_rural=False):
"""
Constructor of the Dataset class. Initialize attributes.
@@ -43,6 +43,7 @@ class Dataset:
else: self.env = "building_type"
self.train_size, self.test_size = check_train_test_percentages(train_size, test_size)
self.outliers = outliers
+ self.remove_rural = remove_rural
def init_all_in_one(self):
"""
@@ -50,8 +51,8 @@ class Dataset:
When the type is "unsupervised", split data into X and Y is not relevant (as there is no train/test sets).
"""
if self.type == "supervised":
- if self.outliers:
- self.remove_outliers()
+ if self.outliers: self.remove_outliers()
+ if self.remove_rural: self.remove_rural_iris()
self.init_X()
self.init_Y()
self.train_test()
diff --git a/predihood/predict.py b/predihood/predict.py
index d0c2e278..c97e7965 100644
--- a/predihood/predict.py
+++ b/predihood/predict.py
@@ -61,9 +61,9 @@ def compute_all_accuracies(data, clf, train_size, test_size, remove_outliers=Fal
results[env] = OrderedDict()
log.debug("--- %s ---", env)
- dataset = Dataset(data_not_filtered, env, selected_indicators=data_not_filtered.indicators, train_size=train_size, test_size=test_size, outliers=remove_outliers, _type='supervised')
+ dataset = Dataset(data_not_filtered, env, selected_indicators=data_not_filtered.indicators, train_size=train_size, test_size=test_size, outliers=remove_outliers, remove_rural=remove_rural, _type='supervised')
dataset.init_all_in_one()
- if remove_rural: dataset.remove_rural_iris()
+ # if remove_rural: dataset.remove_rural_iris()
mean_classifier = 0.0
algo = MethodPrediction(name="", dataset=dataset, classifier=clf)
@@ -78,9 +78,9 @@ def compute_all_accuracies(data, clf, train_size, test_size, remove_outliers=Fal
log.info("accuracy for %s without filtering: %f", env, algo.accuracy)
for top_k, lst in lists.items():
- dataset = Dataset(data, env, selected_indicators=lst[env], train_size=train_size, test_size=test_size, outliers=remove_outliers, _type='supervised')
+ dataset = Dataset(data, env, selected_indicators=lst[env], train_size=train_size, test_size=test_size, outliers=remove_outliers, remove_rural=remove_rural, _type='supervised')
dataset.init_all_in_one()
- if remove_rural: dataset.remove_rural_iris()
+ # if remove_rural: dataset.remove_rural_iris()
algo2 = MethodPrediction(name='', dataset=dataset, classifier=clf)
algo2.fit()
algo2.compute_performance()
--
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