From 74ce73a83b2a93963873f454a376b98167ec815d Mon Sep 17 00:00:00 2001
From: Baptiste Bauvin <baptiste.bauvin@lis-lab.fr>
Date: Mon, 20 Jan 2020 14:17:28 +0100
Subject: [PATCH] Result analysis perfectible
---
config_files/config_test.yml | 18 +++++++++---------
.../result_analysis.py | 4 ++--
.../tests/test_ResultAnalysis.py | 2 +-
3 files changed, 12 insertions(+), 12 deletions(-)
diff --git a/config_files/config_test.yml b/config_files/config_test.yml
index 104cb0a0..f54013c7 100644
--- a/config_files/config_test.yml
+++ b/config_files/config_test.yml
@@ -1,11 +1,11 @@
# The base configuration of the benchmark
Base :
log: True
- name: ["plausible"]
+ name: ["outliers_dset"]
label: "_"
type: ".hdf5"
views:
- pathf: "../data/"
+ pathf: "/home/baptiste/Documents/Datasets/Generated/outliers_dset/"
nice: 0
random_state: 42
nb_cores: 1
@@ -18,18 +18,18 @@ Base :
# All the classification-realted configuration options
Classification:
multiclass_method: "oneVersusOne"
- split: 0.4
+ split: 0.2
nb_folds: 2
nb_class: 2
classes:
- type: ["monoview"]
- algos_monoview: ["adaboost",]
- algos_multiview: ["svm_jumbo_fusion"]
- stats_iter: 2
+ type: ["monoview", "multiview"]
+ algos_monoview: ["decision_tree", "adaboost", "svm_linear", "random_forest"]
+ algos_multiview: ["weighted_linear_early_fusion", "difficulty_fusion", "double_fault_fusion"]
+ stats_iter: 30
metrics: ["accuracy_score", "f1_score"]
- metric_princ: "f1_score"
+ metric_princ: "accuracy_score"
hps_type: "randomized_search-equiv"
- hps_iter: 1
+ hps_iter: 5
#####################################
diff --git a/multiview_platform/mono_multi_view_classifiers/result_analysis.py b/multiview_platform/mono_multi_view_classifiers/result_analysis.py
index e33f1a72..f529f1af 100644
--- a/multiview_platform/mono_multi_view_classifiers/result_analysis.py
+++ b/multiview_platform/mono_multi_view_classifiers/result_analysis.py
@@ -7,6 +7,7 @@ import yaml
import matplotlib as mpl
from matplotlib.patches import Patch
+
# Import third party modules
import matplotlib.pyplot as plt
import numpy as np
@@ -213,7 +214,7 @@ def plot_2d(data, classifiers_names, nbClassifiers, nbExamples,
plt.close()
### The following part is used to generate an interactive graph.
if use_plotly:
- label_index_list = [np.where(labels==i)[0] for i in np.unique(labels)]
+ label_index_list = [np.arange(len(labels))] #[np.where(labels==i)[0] for i in np.unique(labels)]
hover_text = [[example_ids[i] + " failed "+ str(stats_iter-data[i,j])+" time(s)"
for j in range(data.shape[1])]
for i in range(data.shape[0]) ]
@@ -732,7 +733,6 @@ def analyze_biclass(results, benchmark_argument_dictionaries, stats_iter, metric
metrics_scores = get_metrics_scores_biclass(metrics, result)
example_errors = get_example_errors_biclass(arguments["labels"], result)
feature_importances = get_feature_importances(result)
- print(feature_importances)
directory = arguments["directory"]
database_name = arguments["args"]["Base"]["name"]
diff --git a/multiview_platform/tests/test_ResultAnalysis.py b/multiview_platform/tests/test_ResultAnalysis.py
index dff6a23f..6fb4b0d7 100644
--- a/multiview_platform/tests/test_ResultAnalysis.py
+++ b/multiview_platform/tests/test_ResultAnalysis.py
@@ -182,7 +182,7 @@ class Test_gen_error_data(unittest.TestCase):
class Test_format_previous_results(unittest.TestCase):
def test_simple(self):
- biclass_results = {"01":{"metrics_scores":[], "example_errors":[], "feature_importances":[]}}
+ biclass_results = {"01":{"metrics_scores":[], "example_errors":[], "feature_importances":[], "labels":[]}}
random_state = np.random.RandomState(42)
# Gen metrics data
--
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