diff --git a/config_files/config_test.yml b/config_files/config_test.yml
index 4969960bedb6e21f1ddeff7572fa3a10aeff9ae2..ecf24d313bfdf025fcfbe4e023839cf7ddffba96 100644
--- a/config_files/config_test.yml
+++ b/config_files/config_test.yml
@@ -4,7 +4,7 @@ Base :
name: ["plausible"]
label: "_"
type: ".hdf5"
- views: ["300nm", "350nm"]
+ views:
pathf: "../data/"
nice: 0
random_state: 42
@@ -22,8 +22,8 @@ Classification:
nb_folds: 2
nb_class: 2
classes:
- type: ["multiview",]
- algos_monoview: ["decision_tree"]
+ type: ["multiview","monoview"]
+ algos_monoview: ["decision_tree", "adaboost"]
algos_multiview: ["weighted_linear_early_fusion"]
stats_iter: 2
metrics: ["accuracy_score", "f1_score"]
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview/exec_classif_mono_view.py b/multiview_platform/mono_multi_view_classifiers/monoview/exec_classif_mono_view.py
index 875e3763ab77ea4ea771e6653aa80e2242b87ef2..8e6584efa2420e47ed890b7d93b676e0072eb9a2 100644
--- a/multiview_platform/mono_multi_view_classifiers/monoview/exec_classif_mono_view.py
+++ b/multiview_platform/mono_multi_view_classifiers/monoview/exec_classif_mono_view.py
@@ -137,7 +137,7 @@ def exec_monoview(directory, X, Y, name, labels_names, classificationIndices,
testFoldsPreds = y_train_pred
return monoview_utils.MonoviewResult(viewIndex, classifier_name, feat, metricsScores,
full_pred, clKWARGS,
- y_test_multiclass_pred, testFoldsPreds)
+ y_test_multiclass_pred, testFoldsPreds, classifier, X_train.shape[1])
# return viewIndex, [CL_type, feat, metricsScores, full_labels_pred, clKWARGS, y_test_multiclass_pred, testFoldsPreds]
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview/monoview_utils.py b/multiview_platform/mono_multi_view_classifiers/monoview/monoview_utils.py
index c6d76a59db75aa4e3a891b92372c90734901396e..1ded8302d6741bb9e7312c2352bcb4c638be51e6 100644
--- a/multiview_platform/mono_multi_view_classifiers/monoview/monoview_utils.py
+++ b/multiview_platform/mono_multi_view_classifiers/monoview/monoview_utils.py
@@ -160,7 +160,7 @@ class BaseMonoviewClassifier(BaseEstimator, ClassifierMixin):
else:
return str(self.get_params()[param_name])
- def getFeatureImportance(self, directory, nb_considered_feats=50):
+ def get_feature_importance(self, directory, nb_considered_feats=50):
"""Used to generate a graph and a pickle dictionary representing feature importances"""
featureImportances = self.feature_importances_
sortedArgs = np.argsort(-featureImportances)
@@ -206,7 +206,7 @@ def percent(x, pos):
class MonoviewResult(object):
def __init__(self, view_index, classifier_name, view_name, metrics_scores,
full_labels_pred,
- classifier_config, y_test_multiclass_pred, test_folds_preds):
+ classifier_config, y_test_multiclass_pred, test_folds_preds, classifier, n_features):
self.view_index = view_index
self.classifier_name = classifier_name
self.view_name = view_name
@@ -215,6 +215,8 @@ class MonoviewResult(object):
self.classifier_config = classifier_config
self.y_test_multiclass_pred = y_test_multiclass_pred
self.test_folds_preds = test_folds_preds
+ self.clf = classifier
+ self.n_features = n_features
def get_classifier_name(self):
return self.classifier_name + "-" + self.view_name
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/adaboost.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/adaboost.py
index 61d46d0c21a4f3e950158028b16b0d7a415a08df..1e7beb5d9981317ce7dd0bd5a4aed53d5fa5c975 100644
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/adaboost.py
+++ b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/adaboost.py
@@ -135,7 +135,7 @@ class Adaboost(AdaBoostClassifier, BaseMonoviewClassifier):
def getInterpret(self, directory, y_test):
interpretString = ""
- interpretString += self.getFeatureImportance(directory)
+ interpretString += self.get_feature_importance(directory)
interpretString += "\n\n Estimator error | Estimator weight\n"
interpretString += "\n".join(
[str(error) + " | " + str(weight / sum(self.estimator_weights_)) for
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/decision_tree.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/decision_tree.py
index 0e3e2f3e5ca55311c49a6f95a9535bf2045c6ce8..7fe9bdadd3b2ed9e3b65ce49aa6da8e0ba00ffa8 100644
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/decision_tree.py
+++ b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/decision_tree.py
@@ -33,7 +33,7 @@ class DecisionTree(DecisionTreeClassifier, BaseMonoviewClassifier):
def getInterpret(self, directory, y_test):
interpretString = ""
- interpretString += self.getFeatureImportance(directory)
+ interpretString += self.get_feature_importance(directory)
return interpretString
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/gradient_boosting.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/gradient_boosting.py
index 8e33848eb269c0db28a747fae8267744fb7acc08..441aaebf49f1040254ae4ca77ee4fecd8c0d0d18 100644
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/gradient_boosting.py
+++ b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/gradient_boosting.py
@@ -74,7 +74,7 @@ class GradientBoosting(GradientBoostingClassifier, BaseMonoviewClassifier):
def getInterpret(self, directory, y_test):
interpretString = ""
- interpretString += self.getFeatureImportance(directory)
+ interpretString += self.get_feature_importance(directory)
step_test_metrics = np.array(
[self.plotted_metric.score(y_test, step_pred) for step_pred in
self.step_predictions])
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/random_forest.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/random_forest.py
index a829087041dcce3d9b120dc6bf85eda45eb56426..91ebe9dfd19fca77db12e2ca2b8146d1b0f4b288 100644
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/random_forest.py
+++ b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/random_forest.py
@@ -86,7 +86,7 @@ class RandomForest(RandomForestClassifier, BaseMonoviewClassifier):
string for interpretation interpret_string
"""
interpret_string = ""
- interpret_string += self.getFeatureImportance(directory)
+ interpret_string += self.get_feature_importance(directory)
return interpret_string
diff --git a/multiview_platform/mono_multi_view_classifiers/result_analysis.py b/multiview_platform/mono_multi_view_classifiers/result_analysis.py
index f93d72a633ba4dd9e074a18438f95972cd598600..02514a8a538c6363ebe84b5666654fba645e65ba 100644
--- a/multiview_platform/mono_multi_view_classifiers/result_analysis.py
+++ b/multiview_platform/mono_multi_view_classifiers/result_analysis.py
@@ -10,9 +10,11 @@ from matplotlib.patches import Patch
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
+import plotly
# Import own Modules
-from . import metrics
+from .monoview.monoview_utils import MonoviewResult
+from .multiview.multiview_utils import MultiviewResult
# Author-Info
__author__ = "Baptiste Bauvin"
@@ -181,7 +183,6 @@ def plot_2d(data, classifiers_names, nbClassifiers, nbExamples,
plt.close()
### The following part is used to generate an interactive graph.
if use_plotly:
- import plotly
hover_text = [["Failed "+ str(stats_iter-data[i,j])+" time(s)"
for j in range(data.shape[1])]
for i in range(data.shape[0]) ]
@@ -559,6 +560,41 @@ def publishExampleErrors(example_errors, directory, databaseName, labels_names,
logging.debug("Done:\t Biclass Label analysis figures generation")
+def publish_feature_importances(feature_importances, directory, database_name, labels_names, feature_stds=None):
+ for view_name, feature_importance in feature_importances.items():
+ file_name = directory + time.strftime(
+ "%Y_%m_%d-%H_%M_%S") + "-" + database_name + "-" + "_vs_".join(
+ labels_names) + "-" + view_name + "-feature_importances"
+ if feature_stds is not None:
+ feature_std = feature_stds[view_name]
+ feature_std.to_csv(file_name+"_dataframe_stds.csv")
+ else:
+ feature_std = pd.DataFrame(data=np.zeros(feature_importance.shape),
+ index=feature_importance.index,
+ columns=feature_importance.columns)
+ feature_importance.to_csv(file_name+"_dataframe.csv")
+ hover_text = [["-Feature :" + str(feature_name) +
+ "<br>-Classifier : "+classifier_name+
+ "<br>-Importance : "+str(feature_importance.loc[feature_name][classifier_name])+
+ "<br>-STD : " + str(feature_std.loc[feature_name][classifier_name])
+ for classifier_name in list(feature_importance.columns)]
+ for feature_name in list(feature_importance.index)]
+ fig = plotly.graph_objs.Figure(data=plotly.graph_objs.Heatmap(
+ x=list(feature_importance.columns),
+ y=list(feature_importance.index),
+ z=feature_importance.values,
+ text=hover_text,
+ hoverinfo=["text"],
+ colorscale="Greys",
+ reversescale=False))
+ fig.update_layout(
+ xaxis={"showgrid": False, "showticklabels": False, "ticks": ''},
+ yaxis={"showgrid": False, "showticklabels": False, "ticks": ''})
+ plotly.offline.plot(fig, filename=file_name + ".html", auto_open=False)
+
+ del fig
+
+
def get_arguments(benchmark_argument_dictionaries, flag):
r"""Used to get the arguments passed to the benchmark executing function corresponding to the flag of a
biclass experimentation.
@@ -580,6 +616,32 @@ def get_arguments(benchmark_argument_dictionaries, flag):
return benchmarkArgumentDictionary
+def get_feature_importances(result, feature_names=None):
+ r"""Extracts the feature importance from the monoview results and stores them in a dictionnary :
+ feature_importance[view_name] is a pandas.DataFrame of size n_feature*n_clf
+ containing a score of importance for each feature.
+
+ Parameters
+ ----------
+ result : list of results
+
+ Returns
+ -------
+ feature_importances : dict of pd.DataFrame
+ The dictionary containing all the feature importance for each view as pandas DataFrames
+ """
+ feature_importances = {}
+ for classifier_result in result:
+ if isinstance(classifier_result, MonoviewResult):
+ if classifier_result.view_name not in feature_importances:
+ feature_importances[classifier_result.view_name] = pd.DataFrame(index=feature_names)
+ if hasattr(classifier_result.clf, 'feature_importances_'):
+ feature_importances[classifier_result.view_name][classifier_result.classifier_name] = classifier_result.clf.feature_importances_
+ else:
+ feature_importances[classifier_result.view_name][classifier_result.classifier_name] = np.zeros(classifier_result.n_features)
+ return feature_importances
+
+
def analyze_biclass(results, benchmark_argument_dictionaries, stats_iter, metrics, example_ids):
r"""Used to extract and format the results of the different biclass experimentations performed.
@@ -616,6 +678,7 @@ 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)
directory = arguments["directory"]
@@ -627,14 +690,18 @@ def analyze_biclass(results, benchmark_argument_dictionaries, stats_iter, metric
labels_names)
publishExampleErrors(example_errors, directory, database_name,
labels_names, example_ids)
+ publish_feature_importances(feature_importances, directory, database_name, labels_names)
if not str(classifierPositive) + str(classifierNegative) in biclass_results:
biclass_results[str(classifierPositive) + str(classifierNegative)] = {}
biclass_results[str(classifierPositive) + str(classifierNegative)][
"metrics_scores"] = [i for i in range(stats_iter)]
biclass_results[str(classifierPositive) + str(classifierNegative)][
"example_errors"] = [i for i in range(stats_iter)]
+ biclass_results[str(classifierPositive) + str(classifierNegative)][
+ "feature_importances"] = [i for i in range(stats_iter)]
biclass_results[str(classifierPositive) + str(classifierNegative)]["metrics_scores"][iteridex] = metrics_scores
biclass_results[str(classifierPositive) + str(classifierNegative)]["example_errors"][iteridex] = example_errors
+ biclass_results[str(classifierPositive) + str(classifierNegative)]["feature_importances"][iteridex] = feature_importances
logging.debug("Done:\t Analzing all biclass resuls")
return results, biclass_results
@@ -981,25 +1048,41 @@ def format_previous_results(biclass_results):
"""
metrics_analysis = dict((key, {}) for key in biclass_results.keys())
error_analysis = dict((key, {}) for key in biclass_results.keys())
+ feature_importances_analysis = dict((key, {}) for key in biclass_results.keys())
+ feature_importances_stds = dict((key, {}) for key in biclass_results.keys())
for label_combination, biclass_result in biclass_results.items():
- concat_dict = {}
+ metric_concat_dict = {}
for iter_index, metrics_score in enumerate(
biclass_result["metrics_scores"]):
for metric_name, dataframe in metrics_score.items():
- if metric_name not in concat_dict:
- concat_dict[metric_name] = dataframe
+ if metric_name not in metric_concat_dict:
+ metric_concat_dict[metric_name] = dataframe
else:
- concat_dict[metric_name] = pd.concat(
- [concat_dict[metric_name], dataframe])
+ metric_concat_dict[metric_name] = pd.concat(
+ [metric_concat_dict[metric_name], dataframe])
- for metric_name, dataframe in concat_dict.items():
+ for metric_name, dataframe in metric_concat_dict.items():
metrics_analysis[label_combination][metric_name] = {}
metrics_analysis[label_combination][metric_name][
"mean"] = dataframe.groupby(dataframe.index).mean()
metrics_analysis[label_combination][metric_name][
"std"] = dataframe.groupby(dataframe.index).std(ddof=0)
+ importance_concat_dict = {}
+ for iter_index, view_feature_importances in enumerate(biclass_result["feature_importances"]):
+ for view_name, feature_importances in view_feature_importances.items():
+ if view_name not in importance_concat_dict:
+ importance_concat_dict[view_name] = feature_importances
+ else:
+ importance_concat_dict[view_name] = pd.concat(
+ [importance_concat_dict[view_name], feature_importances])
+
+ for view_name, dataframe in importance_concat_dict.items():
+ feature_importances_analysis[label_combination][view_name] = dataframe.groupby(dataframe.index).mean()
+
+ feature_importances_stds[label_combination][view_name] = dataframe.groupby(dataframe.index).std(ddof=0)
+
added_example_errors = {}
for example_errors in biclass_result["example_errors"]:
for classifier_name, errors in example_errors.items():
@@ -1008,13 +1091,13 @@ def format_previous_results(biclass_results):
else:
added_example_errors[classifier_name] += errors
error_analysis[label_combination] = added_example_errors
- return metrics_analysis, error_analysis
+ return metrics_analysis, error_analysis, feature_importances_analysis, feature_importances_stds
def analyzebiclass_iter(biclass_results, stats_iter, directory,
labels_dictionary, data_base_name, example_ids):
"""Used to format the results in order to plot the mean results on the iterations"""
- metrics_analysis, error_analysis = format_previous_results(biclass_results)
+ metrics_analysis, error_analysis, feature_improtances, feature_improtances_stds = format_previous_results(biclass_results)
results = publish_iter_biclass_metrics_scores(metrics_analysis,
directory, labels_dictionary,
@@ -1022,6 +1105,12 @@ def analyzebiclass_iter(biclass_results, stats_iter, directory,
publish_iter_biclass_example_errors(error_analysis, directory,
labels_dictionary,
stats_iter, example_ids)
+ for label_combination, feature_improtances_view in feature_improtances.items():
+ labels = [labels_dictionary[
+ int(label_combination[0])], labels_dictionary[
+ int(label_combination[1])]]
+ publish_feature_importances(feature_improtances_view, os.path.join(directory,"-vs-".join(labels)+"/"),
+ data_base_name, labels, feature_improtances_stds[label_combination])
return results
def analyze_iter_multiclass(multiclass_results, directory, stats_iter, metrics,