diff --git a/config_files/config_test.yml b/config_files/config_test.yml
index 0a83c99239acbf7070a742a076a4a0e946151526..f400a1855633ac093b2703f30f477330e27e224b 100644
--- a/config_files/config_test.yml
+++ b/config_files/config_test.yml
@@ -13,7 +13,7 @@ debug: True
add_noise: False
noise_std: 0.0
res_dir: "../results/"
-track_tracebacks: True
+track_tracebacks: False
# All the classification-realted configuration options
multiclass_method: "oneVersusOne"
@@ -21,13 +21,13 @@ split: 0.49
nb_folds: 2
nb_class: 2
classes:
-type: ["multiview", "monoview"]
+type: ["multiview"]
algos_monoview: ["decision_tree" ]
algos_multiview: ["weighted_linear_early_fusion",]
stats_iter: 2
metrics: ["accuracy_score", "f1_score"]
metric_princ: "accuracy_score"
-hps_type: "Random"
+hps_type: "None"
hps_args:
n_iter: 4
equivalent_draws: False
@@ -48,6 +48,34 @@ hps_args:
######################################
## The Monoview Classifier arguments #
######################################
+weighted_linear_early_fusion:
+ monoview_classifier_config:
+ init: CustomDecisionTree
+ init__ccp_alpha: 0.0
+ init__class_weight: null
+ init__criterion: gini
+ init__max_depth: 1
+ init__max_features: null
+ init__max_leaf_nodes: null
+ init__min_impurity_decrease: 0.0
+ init__min_impurity_split: null
+ init__min_samples_leaf: 1
+ init__min_samples_split: 2
+ init__min_weight_fraction_leaf: 0.0
+ init__presort: deprecated
+ init__random_state: null
+ init__splitter: best
+ loss: exponential
+ max_depth: 5
+ n_estimators: 412
+ monoview_classifier_name: gradient_boosting
+ view_weights:
+ - 0.16666666666666669
+ - 0.16666666666666669
+ - 0.16666666666666669
+ - 0.16666666666666669
+ - 0.16666666666666669
+ - 0.16666666666666669
#
#random_forest:
# n_estimators: [25]
diff --git a/multiview_platform/mono_multi_view_classifiers/exec_classif.py b/multiview_platform/mono_multi_view_classifiers/exec_classif.py
index 084b01dcb1435a5d0457e7155650fb0b6cdc4e0a..acb47b7511685a5eb947d9c9e97e68de4afaae0f 100644
--- a/multiview_platform/mono_multi_view_classifiers/exec_classif.py
+++ b/multiview_platform/mono_multi_view_classifiers/exec_classif.py
@@ -199,7 +199,7 @@ def init_monoview_exps(classifier_names,
def gen_single_monoview_arg_dictionary(classifier_name, arguments, nb_class,
view_index, view_name, hps_kwargs):
if classifier_name in arguments:
- classifier_config = dict((key, value[0]) for key, value in arguments[
+ classifier_config = dict((key, value) for key, value in arguments[
classifier_name].items())
else:
classifier_config = {}
@@ -227,10 +227,7 @@ def extract_dict(classifier_config):
"""Reverse function of get_path_dict"""
extracted_dict = {}
for key, value in classifier_config.items():
- if isinstance(value, list):
- extracted_dict = set_element(extracted_dict, key, value[0])
- else:
- extracted_dict = set_element(extracted_dict, key, value)
+ extracted_dict = set_element(extracted_dict, key, value)
return extracted_dict
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 fc1f7c37b774debe568a15b6bab99e4f9089e354..7136370f1c684ead6010e6a9a944da031fdf4779 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
@@ -16,7 +16,7 @@ __status__ = "Prototype" # Production, Development, Prototype
classifier_class_name = "GradientBoosting"
-class CustomDecisionTree(DecisionTreeClassifier):
+class CustomDecisionTreeGB(DecisionTreeClassifier):
def predict(self, X, check_input=True):
y_pred = DecisionTreeClassifier.predict(self, X,
check_input=check_input)
@@ -27,7 +27,7 @@ class GradientBoosting(GradientBoostingClassifier, BaseMonoviewClassifier):
def __init__(self, random_state=None, loss="exponential", max_depth=1.0,
n_estimators=100,
- init=CustomDecisionTree(max_depth=1),
+ init=CustomDecisionTreeGB(max_depth=1),
**kwargs):
GradientBoostingClassifier.__init__(self,
loss=loss,
diff --git a/multiview_platform/mono_multi_view_classifiers/result_analysis/error_analysis.py b/multiview_platform/mono_multi_view_classifiers/result_analysis/error_analysis.py
index f16be40abfd8691a3f194d4826214d6a5454d843..fd18a3b02edc250479a688084ec4759c7dde6e5f 100644
--- a/multiview_platform/mono_multi_view_classifiers/result_analysis/error_analysis.py
+++ b/multiview_platform/mono_multi_view_classifiers/result_analysis/error_analysis.py
@@ -60,21 +60,22 @@ def publish_example_errors(example_errors, directory, databaseName,
np.savetxt(base_file_name + "bar_plot_data.csv", error_on_examples,
delimiter=",")
- plot_2d(data_2d, classifiers_names, nb_classifiers, nb_examples,
- base_file_name, example_ids=example_ids, labels=labels)
+ plot_2d(data_2d, classifiers_names, nb_classifiers, base_file_name,
+ example_ids=example_ids, labels=labels)
- plot_errors_bar(error_on_examples, nb_classifiers, nb_examples,
- base_file_name)
+ plot_errors_bar(error_on_examples, nb_examples,
+ base_file_name, example_ids=example_ids)
logging.debug("Done:\t Biclass Label analysis figures generation")
+
def publish_all_example_errors(iter_results, directory,
stats_iter,
example_ids, labels):
logging.debug(
"Start:\t Global label analysis figure generation")
- nbExamples, nbClassifiers, data, \
+ nb_examples, nb_classifiers, data, \
error_on_examples, classifier_names = gen_error_data_glob(iter_results,
stats_iter)
@@ -82,17 +83,16 @@ def publish_all_example_errors(iter_results, directory,
np.savetxt(os.path.join(directory, "example_errors.csv"), error_on_examples,
delimiter=",")
- plot_2d(data, classifier_names, nbClassifiers, nbExamples,
+ plot_2d(data, classifier_names, nb_classifiers,
os.path.join(directory, ""), stats_iter=stats_iter,
example_ids=example_ids, labels=labels)
- plot_errors_bar(error_on_examples, nbClassifiers * stats_iter,
- nbExamples, os.path.join(directory, ""))
+ plot_errors_bar(error_on_examples, nb_examples, os.path.join(directory, ""),
+ example_ids=example_ids)
logging.debug(
"Done:\t Global label analysis figures generation")
-
def gen_error_data(example_errors):
r"""Used to format the error data in order to plot it efficiently. The
data is saves in a `.csv` file.
@@ -106,14 +106,6 @@ def gen_error_data(example_errors):
- 1 if the classifier `<classifier_name>` classifier well the example,
- 0 if it fail to classify the example,
- -100 if it did not classify the example (multiclass one versus one).
- base_file_name : list of str
- The name of the file in which the figure will be saved
- ("2D_plot_data.csv" and "bar_plot_data.csv" will
- be added at the end).
- nbCopies : int, optinal, default: 2
- The number of times the data is copied (classifier wise) in order for
- the figure to be more readable.
-
Returns
-------
@@ -146,9 +138,10 @@ def gen_error_data(example_errors):
except:
import pdb;
pdb.set_trace()
- error_on_examples = -1 * np.sum(data_2d, axis=1) / nb_classifiers
+ error_on_examples = np.sum(data_2d, axis=1) / nb_classifiers
return nb_classifiers, nb_examples, classifiers_names, data_2d, error_on_examples
+
def gen_error_data_glob(iter_results, stats_iter):
nb_examples = next(iter(iter_results.values())).shape[0]
nb_classifiers = len(iter_results)
@@ -158,16 +151,14 @@ def gen_error_data_glob(iter_results, stats_iter):
iter_results.items()):
data[:, clf_index] = error_data
classifier_names.append(classifier_name)
- error_on_examples = -1 * np.sum(data, axis=1) + (
+ error_on_examples = np.sum(data, axis=1) / (
nb_classifiers * stats_iter)
return nb_examples, nb_classifiers, data, error_on_examples, \
classifier_names
-def plot_2d(data, classifiers_names, nbClassifiers, nbExamples,
- file_name, minSize=10, labels=None,
- width_denominator=2.0, height_denominator=20.0, stats_iter=1,
- use_plotly=True, example_ids=None):
+def plot_2d(data, classifiers_names, nb_classifiers, file_name, labels=None,
+ stats_iter=1, use_plotly=True, example_ids=None):
r"""Used to generate a 2D plot of the errors.
Parameters
@@ -177,12 +168,8 @@ def plot_2d(data, classifiers_names, nbClassifiers, nbExamples,
and -100 if the example was not classified.
classifiers_names : list of str
The names of the classifiers.
- nbClassifiers : int
+ nb_classifiers : int
The number of classifiers.
- nbExamples : int
- The number of examples.
- nbCopies : int
- The number of times the data is copied (classifier wise) in order for the figure to be more readable
file_name : str
The name of the file in which the figure will be saved ("error_analysis_2D.png" will be added at the end)
minSize : int, optinal, default: 10
@@ -198,13 +185,18 @@ def plot_2d(data, classifiers_names, nbClassifiers, nbExamples,
-------
"""
fig, ax = plt.subplots(nrows=1, ncols=1, )
+ label_index_list = np.concatenate([np.where(labels == i)[0] for i in
+ np.unique(
+ labels)])
cmap, norm = iter_cmap(stats_iter)
- cax = plt.imshow(data, cmap=cmap, norm=norm,
+ cax = plt.imshow(data[np.flip(label_index_list), :], cmap=cmap, norm=norm,
aspect='auto')
plt.title('Errors depending on the classifier')
- ticks = np.arange(0, nbClassifiers, 1)
+ ticks = np.arange(0, nb_classifiers, 1)
tick_labels = classifiers_names
plt.xticks(ticks, tick_labels, rotation="vertical")
+ plt.yticks([], [])
+ plt.ylabel("Examples")
cbar = fig.colorbar(cax, ticks=[-100 * stats_iter / 2, 0, stats_iter])
cbar.ax.set_yticklabels(['Unseen', 'Always Wrong', 'Always Right'])
@@ -213,9 +205,7 @@ 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.concatenate([np.where(labels == i)[0] for i in
- np.unique(
- labels)]) # [np.where(labels==i)[0] for i in np.unique(labels)]
+ # [np.where(labels==i)[0] for i in np.unique(labels)]
hover_text = [[example_ids[example_index] + " failed " + str(
stats_iter - data[
example_index, classifier_index]) + " time(s), labelled " + str(
@@ -223,7 +213,6 @@ def plot_2d(data, classifiers_names, nbClassifiers, nbExamples,
for classifier_index in range(data.shape[1])]
for example_index in range(data.shape[0])]
fig = plotly.graph_objs.Figure()
- # for row_index, label_index in enumerate(label_index_list):
fig.add_trace(plotly.graph_objs.Heatmap(
x=list(classifiers_names),
y=[example_ids[label_ind] for label_ind in label_index_list],
@@ -234,8 +223,7 @@ def plot_2d(data, classifiers_names, nbClassifiers, nbExamples,
colorbar=dict(tickvals=[0, stats_iter],
ticktext=["Always Wrong", "Always Right"]),
reversescale=True), )
- fig.update_yaxes(title_text="Examples", showticklabels=False, ticks='')
- fig.update_xaxes(showticklabels=False, )
+ fig.update_yaxes(title_text="Examples", showticklabels=True)
fig.update_layout(paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)')
fig.update_xaxes(showticklabels=True, )
@@ -244,7 +232,8 @@ def plot_2d(data, classifiers_names, nbClassifiers, nbExamples,
del fig
-def plot_errors_bar(error_on_examples, nbClassifiers, nbExamples, fileName):
+def plot_errors_bar(error_on_examples, nb_examples, file_name,
+ use_plotly=True, example_ids=None):
r"""Used to generate a barplot of the muber of classifiers that failed to classify each examples
Parameters
@@ -253,23 +242,28 @@ def plot_errors_bar(error_on_examples, nbClassifiers, nbExamples, fileName):
An array counting how many classifiers failed to classifiy each examples.
classifiers_names : list of str
The names of the classifiers.
- nbClassifiers : int
+ nb_classifiers : int
The number of classifiers.
- nbExamples : int
+ nb_examples : int
The number of examples.
- fileName : str
+ file_name : str
The name of the file in which the figure will be saved ("error_analysis_2D.png" will be added at the end)
Returns
-------
"""
fig, ax = plt.subplots()
- x = np.arange(nbExamples)
+ x = np.arange(nb_examples)
plt.bar(x, error_on_examples)
- plt.ylim([0, nbClassifiers])
plt.title("Number of classifiers that failed to classify each example")
- fig.savefig(fileName + "error_analysis_bar.png", transparent=True)
+ fig.savefig(file_name + "error_analysis_bar.png", transparent=True)
plt.close()
+ if use_plotly:
+ fig = plotly.graph_objs.Figure([plotly.graph_objs.Bar(x=example_ids, y=error_on_examples)])
+ plotly.offline.plot(fig, filename=file_name + "error_analysis_bar.html",
+ auto_open=False)
+
+
def iter_cmap(statsIter):
diff --git a/multiview_platform/mono_multi_view_classifiers/utils/hyper_parameter_search.py b/multiview_platform/mono_multi_view_classifiers/utils/hyper_parameter_search.py
index 734092cd1d07175bc72d663bdb47e54eb3e2727a..d65af38d05c9d67ff071c67e1eac7c97a937c765 100644
--- a/multiview_platform/mono_multi_view_classifiers/utils/hyper_parameter_search.py
+++ b/multiview_platform/mono_multi_view_classifiers/utils/hyper_parameter_search.py
@@ -1,6 +1,7 @@
import itertools
import sys
import traceback
+import yaml
from abc import abstractmethod
import matplotlib.pyplot as plt
@@ -8,7 +9,7 @@ import numpy as np
from scipy.stats import randint, uniform
from sklearn.model_selection import RandomizedSearchCV, GridSearchCV, \
ParameterGrid, ParameterSampler
-from sklearn.base import clone
+from sklearn.base import clone, BaseEstimator
from .multiclass import MultiClassWrapper
from .organization import secure_file_path
@@ -132,9 +133,8 @@ class HPSearch:
scores_array = scores_array[sorted_indices]
output_string = ""
for parameters, score in zip(tested_params, scores_array):
- if "random_state" in parameters:
- parameters.pop("random_state")
- output_string += "\n{}\t\t{}".format(parameters, score)
+ formatted_params = format_params(parameters)
+ output_string += "\n{}\n\t\t{}".format(yaml.dump(formatted_params), score)
if self.tracebacks:
output_string += "Failed : \n\n\n"
for traceback, params in zip(self.tracebacks, self.tracebacks_params):
@@ -417,6 +417,20 @@ class CustomUniform:
return unif
+def format_params(params):
+ if isinstance(params, dict):
+ return dict((key, format_params(value))
+ for key, value in params.items()
+ if key!="random_state")
+ elif isinstance(params, BaseEstimator):
+ return params.__class__.__name__
+ elif isinstance(params, np.ndarray):
+ return [float(param) for param in params]
+ elif isinstance(params, np.float64):
+ return float(params)
+ else:
+ return params
+
# def randomized_search_(dataset_var, labels, classifier_package, classifier_name,
# metrics_list, learning_indices, k_folds, random_state,