From 349c8ed16f79d756f9768125a880760456bcb421 Mon Sep 17 00:00:00 2001
From: Baptiste Bauvin <baptiste.bauvin@lis-lab.fr>
Date: Tue, 28 Jan 2020 11:10:59 +0100
Subject: [PATCH] Added information per view control
---
generator/update_baptiste.py | 32 ++++++++++++++++++++++++++++----
1 file changed, 28 insertions(+), 4 deletions(-)
diff --git a/generator/update_baptiste.py b/generator/update_baptiste.py
index d26fa60..726e4ed 100644
--- a/generator/update_baptiste.py
+++ b/generator/update_baptiste.py
@@ -6,6 +6,7 @@ from math import ceil, floor
import pandas as pd
import h5py
+
class MultiviewDatasetGenetator():
def __init__(self, n_samples=100, n_views=2, n_classes=2,
@@ -19,6 +20,7 @@ class MultiviewDatasetGenetator():
standard_deviation=2,
weights=None,
flip_y=0.0,
+ n_informative_weights=None,
random_state=42, config_path=None):
if config_path is not None:
with open(config_path) as config_file:
@@ -38,6 +40,7 @@ class MultiviewDatasetGenetator():
self.standard_deviation = standard_deviation
self.weights = weights
self.flip_y = flip_y
+ self.n_informative_weights = n_informative_weights
if isinstance(random_state, np.random.RandomState):
self.random_state = random_state
elif isinstance(random_state, int):
@@ -97,20 +100,39 @@ class MultiviewDatasetGenetator():
flip_y=self.flip_y,
class_sep=self.n_clusters_per_class * self.class_sep_factor,
random_state=self.random_state, shuffle=False)
+ self.informative_indices = np.arange(self.dim_Z)[:self.n_informative]
I_q = np.arange(self.Z.shape[1])
meta_I_v = []
self.results = []
- for view in range(n_views):
+ for view_index in range(n_views):
+ if self.n_informative_weights is not None and len(self.n_informative_weights)==n_views:
+ if self.n_informative*self.n_informative_weights[view_index] > d_v[view_index]:
+ n_informative_view = int(self.n_informative*self.n_informative_weights[view_index])
+ d_v[view_index] = n_informative_view
+ I_v = self.random_state.choice(self.informative_indices,
+ size=n_informative_view,
+ replace=False)
+ else:
+ n_informative_view = int(self.n_informative*self.n_informative_weights[view_index])
+ print(n_informative_view, d_v)
+ informative_indices = self.random_state.choice(self.informative_indices,
+ size=n_informative_view,
+ replace=False)
+ I_v = np.concatenate((informative_indices,
+ self.random_state.choice(np.arange(self.dim_Z)[self.n_informative:],
+ size=d_v[view_index]-n_informative_view,
+ replace=False)))
+ else:
# choice d_v[view] numeros of Z columns uniformly from I_q
- I_v = self.random_state.choice(I_q, size=d_v[view],
- replace=False) # tirage dans I_q sans remise de taille d_v[view]
+ I_v = self.random_state.choice(I_q, size=d_v[view_index],
+ replace=False) # tirage dans I_q sans remise de taille d_v[view]
meta_I_v += list(I_v)
# projection of Z along the columns in I_v
X_v = self.projection( I_v)
self.results.append((X_v, I_v))
# remove R*d_v[view] columns numeros of I_v form I_q
elements_to_remove = self.random_state.choice(I_v,
- size=floor(self.R * d_v[view]),
+ size=floor(self.R * d_v[view_index]),
replace=False) # tirage dans I_v sans remise de taille floor(R*d_v[view])
I_q = np.setdiff1d(I_q,
elements_to_remove) # I_q less elements from elements_to_remove
@@ -250,6 +272,7 @@ if __name__=="__main__":
flip_y = 0.00 # Ratio of label noise
random_state = 42
weights = None # The proportions of examples in each class
+ n_informative_weights = np.array([0.1,0.5,0.9,0.4])
path = "/home/baptiste/Documents/Datasets/Generated/metrics_dset/"
name = "metrics"
@@ -269,6 +292,7 @@ if __name__=="__main__":
standard_deviation=standard_deviation,
flip_y=flip_y,
weights=weights,
+ n_informative_weights=n_informative_weights,
random_state=random_state)
multiview_generator.generate()
--
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