def binarizelabels(self, labels, nclasses=None):
if nclasses == None:
mlb = preprocessing.MultiLabelBinarizer()
return mlb.fit_transform(labels)
# for fit_and_predict to return binarized object of predicted classes
mlb = preprocessing.MultiLabelBinarizer(classes=range(nclasses))
return mlb.fit_transform(labels)
def example():
from sklearn import preprocessing
lb = preprocessing.LabelBinarizer()
lb.fit([1, 2, 6, 4, 2])
print(lb.classes_)
print(lb.transform([1, 6]))
#######################################
lb = preprocessing.MultiLabelBinarizer()
lb.fit_transform([(1, 2), (3, )])
print(lb.classes_)
########################################
from sklearn import preprocessing
le = preprocessing.LabelEncoder()
le.fit([1, 2, 2, 6])
print(le.classes_)
print(le.transform([1, 1, 2, 6]))
print(le.inverse_transform([0, 0, 1, 2]))
#########################################
le = preprocessing.LabelEncoder()
le.fit(["paris", "paris", "tokyo", "amsterdam"])
print(list(le.classes_))
print(le.transform(["tokyo", "tokyo", "paris"]))
print(list(le.inverse_transform([2, 2, 1])))
def analyse_tweet_ml(tweet):
df = pd.read_csv('tweets.csv')
df.isnull().any()
message = tweet
X_train = df.tweet
y_train = df.label.astype(str)
lb = preprocessing.MultiLabelBinarizer()
y_train = lb.fit_transform(y_train)
X_test = np.array([message])
# ML Pipeline
classifier = Pipeline([('vectorizer',
CountVectorizer(max_df=0.5, ngram_range=(1, 2))),
('tfidf', TfidfTransformer()),
('clf', OneVsRestClassifier(LinearSVC()))])
classifier.fit(X_train, y_train)
predicted = classifier.predict(X_test)
print predicted[0]
count = 0
label = ""
for i in predicted[0]:
if i == 1:
label = count
count = count + 1
return label
def _encode_labels(self):
"""Encodes y labels using sklearn to create allow for string or numeric inputs"""
mlb = preprocessing.MultiLabelBinarizer()
mlb.fit(self.y)
mapping_dict = dict(zip(list(range(0, len(mlb.classes_))), mlb.classes_))
return mlb, mapping_dict
def get_top_dataset():
lb = preprocessing.MultiLabelBinarizer(top_categories)
train_data, train_targets = get_top_split_set("train")
test_data, test_targets = get_top_split_set("test")
train_targets = lb.fit_transform(train_targets)
test_targets = lb.transform(test_targets)
return np.array(train_data), np.array(train_targets), np.array(test_data), np.array(test_targets)
def oneVsRest_LogReg_TfIdf(X_train, X_test, Y_train, Y_test, word_dict, tags_dict, data_files, test_doc_ids ):
print('Processing : oneVsRest_LogReg_TfIdf')
print('-'*50)
Y_original = Y_test
vectorizer = CountVectorizer(min_df=1, vocabulary=word_dict)
X_v_train = vectorizer.fit_transform(X_train)
X_v_test = vectorizer.fit_transform(X_test)
transformer = TfidfTransformer(smooth_idf=False)
X_train_tf = transformer.fit_transform(X_v_train)
X_test_tf = transformer.fit_transform(X_v_test)
uniq_tags_names = list(tags_dict.keys())
mlb = preprocessing.MultiLabelBinarizer(classes=uniq_tags_names)
Y_train = mlb.fit_transform(Y_train)
Y_test = mlb.fit_transform(Y_test)
classifier = OneVsRestClassifier(LogisticRegression(penalty='l2', C=0.01))
classifier.fit(X_train_tf, Y_train)
score = classifier.score(X_test_tf, Y_test)
print('-' * 50)
print('Score oneVsRest_LogReg_TfIdf : {}'.format(score))
print('-' * 50)
Y_pred = classifier.predict(X_v_test)
Y_back = mlb.inverse_transform(Y_pred)
write_to_file(Y_original, Y_back, 'oneVsRest_LogREg', score, data_files, test_doc_ids)
def fit(self, X, y, log_run=True):
"""Fit the model to the provided data"""
y = preprocessing.MultiLabelBinarizer().fit_transform(
y.reshape(len(y), 1))
y = y.astype(np.float32)
kwargs = {}
if log_run:
cb = tf.compat.v1.keras.callbacks.TensorBoard(
log_dir=self.tensor_logdir,
#histogram_freq=1,
#write_graph=True,
#write_grads=True,
write_images=True,
)
kwargs['callbacks'] = [cb]
history = self.model.fit(
X,
y,
self.batch_size,
self.n_epoch,
verbose=2,
validation_split=0.1, # XXX
**kwargs)
# The history.history dict contains lists of numpy.float64
# values which don't work well with json. We need to turn them
# into floats.
ret = {}
for k, v in history.history.items():
ret[k] = [float(x) for x in v]
return ret
def train(X, y, outpath=None, verbose=True):
def build(X, y=None):
"""
Inner build function that builds a single model.
"""
model = Pipeline([('preprocessor', NLTKPreprocessor()),
('vectorizer', CountVectorizer()),
('tfidf', TfidfTransformer()),
('clf', OneVsRestClassifier(LinearSVC(C=0.9)))])
model.fit(X, y)
return model
# Label encode the targets
labels = preprocessing.MultiLabelBinarizer()
y = labels.fit_transform(y)
model = build(X, y)
model.labels_ = labels
if outpath:
with open(outpath, 'wb') as f:
pickle.dump(model, f)
if verbose: print("Model written out to {}".format(outpath))
return model
def find_hot_encoders(X, missing_values=None):
""" Find hot encoders for every feature
:param X: A numpy matrix of the data. First axis corresponding to instances, second axis corresponding to samples
:param missing_values: The value for missing values.
:return: Hot encoders to be used for future hot encoding
"""
X = np.asarray(X)
new_X = np.zeros(([X.shape[0], 0]))
hot_encoders = []
for i in range(X.shape[1]):
# Copy a new row and delete the missing values
new_col = np.copy(X[:, i:i + 1])
new_col = np.delete(new_col, new_col == missing_values, axis=0)
# Find out if data is categorical
try:
new_col = new_col.astype(float)
hot_encoder = None
except:
# Create hot encoder en use it for fitting and transformation
hot_encoder = PP.MultiLabelBinarizer()
new_col = hot_encoder.fit_transform(new_col)
# Keep record of the new data and
hot_encoders.append(hot_encoder)
return hot_encoders
def model_inferring(val_path='./pelvis_only_224_test_hot_nonhardware.npz', model_path='./fx_models/nonhardware_fx_sep/final_dense_model'):
image_array, id_array, label_array, orig_idx_array = read_npz_hotlabel(val_path)
model = keras.models.load_model(model_path)
predictions = model.predict(image_array)
custom_eval_models(predictions, label_array)
class_0_preds = predictions[:, 0].reshape(predictions.shape[0], 1)
non_fx_preds = np.sum(predictions[:, 1:], axis=1)
non_fx_preds = non_fx_preds.reshape(non_fx_preds.shape[0], 1)
binary_preds = np.concatenate([class_0_preds, non_fx_preds], axis=1)
ind_labels = np.argmax(label_array, axis=1)
binary_labels = np.where(ind_labels != 0, 1, ind_labels)
binary_labels = preprocessing.MultiLabelBinarizer(np.arange(2)).fit_transform(binary_labels.reshape(binary_labels.shape[0], 1))
custom_eval_models(binary_preds, binary_labels)
np.savez('something',
image_array=image_array,
id_array=id_array,
label_array=label_array,
orig_idx_array=orig_idx_array,
predictions=predictions,
binary_preds=binary_preds,
binary_labels=binary_labels
)
def train(method=0):
print 'command train'
print 'use %s' % CLFS_NAMES[method]
description_list = []
tags_list = []
for line in open('train.data'):
tmp = line.rstrip('\r\n').split('#$#')
description = tmp[1]
tags = tmp[2].rstrip(',').split(
',') # TODO Semantic Web (RDF, OWL, etc.)
description_list.append(description)
tags_list.append(tags)
all_tags = open(
'allTags.txt').read().splitlines() # TODO how to fit this ?
lb = preprocessing.MultiLabelBinarizer()
binary_tags_list = lb.fit_transform(tags_list)
X_train = np.array(description_list)
y_train = binary_tags_list
clf = Pipeline([('vectorizer', CountVectorizer()),
('tfidf', TfidfTransformer()),
('clf', OneVsRestClassifier(CLFS[method]))])
print 'train begin'
clf.fit(X_train, y_train)
print 'train end'
joblib.dump(clf, 'models/model.pkl')
joblib.dump(lb, 'models/lb.pkl')
def OVR_Classify(X_train, X_test, Y_train, word_dict, tags_dict, test_tags=None):
print('Processing : OVR_Classify')
print('-' * 50)
from sklearn.feature_extraction.text import TfidfTransformer
vectorizer = CountVectorizer(min_df=1, vocabulary=word_dict)
X_v_train = vectorizer.fit_transform(X_train)
X_v_test = vectorizer.fit_transform(X_test)
transformer = TfidfTransformer(smooth_idf=False)
X_train_tf = transformer.fit_transform(X_v_train)
X_test_tf = transformer.fit_transform(X_v_test)
#uniq_tags_names = list(tags_dict.keys())
mlb = preprocessing.MultiLabelBinarizer(classes=list(tags_dict))
train_model = mlb.fit_transform(Y_train)
classifier = OneVsRestClassifier(Perceptron(#loss='hinge',
alpha=1e-3,
penalty='elasticnet',
random_state=999,
#class_weight="balanced",
n_iter=50,
#learning_rate='optimal'
))
classifier.fit(X_train_tf, train_model)
print('-' * 50)
#print('Score oneVsRest_SGDC_TfIdf : {}'.format(score))
print('-' * 50)
Y_pred = classifier.predict(X_test_tf)
print(Y_pred)
Y_back = mlb.inverse_transform(Y_pred)
print(Y_back)
def explicitness_per_factor(mus_train, y_train, mus_test, y_test):
"""Compute explicitness score for a factor as ROC-AUC of a classifier.
Args:
mus_train: Representation for training, (num_codes, num_points)-np array.
y_train: Ground truth factors for training, (num_factors, num_points)-np
array.
mus_test: Representation for testing, (num_codes, num_points)-np array.
y_test: Ground truth factors for testing, (num_factors, num_points)-np
array.
Returns:
roc_train: ROC-AUC score of the classifier on training data.
roc_test: ROC-AUC score of the classifier on testing data.
"""
x_train = np.transpose(mus_train)
x_test = np.transpose(mus_test)
# CHANGED: Explicitly use the default params from numpy 0.20 (solver,
# multi_class) to avoid warning messages
clf = linear_model.LogisticRegression(solver='liblinear',
multi_class='ovr').fit(
x_train, y_train)
y_pred_train = clf.predict_proba(x_train)
y_pred_test = clf.predict_proba(x_test)
mlb = preprocessing.MultiLabelBinarizer()
roc_train = metrics.roc_auc_score(
mlb.fit_transform(np.expand_dims(y_train, 1)), y_pred_train)
roc_test = metrics.roc_auc_score(
mlb.fit_transform(np.expand_dims(y_test, 1)), y_pred_test)
return roc_train, roc_test
def explicitness_per_factor(mus_train, y_train, mus_test, y_test):
"""Compute explicitness score for a factor as ROC-AUC of a classifier.
Args:
mus_train: Representation for training, (num_codes, num_points)-np array.
y_train: Ground truth factors for training, (num_factors, num_points)-np
array.
mus_test: Representation for testing, (num_codes, num_points)-np array.
y_test: Ground truth factors for testing, (num_factors, num_points)-np
array.
Returns:
roc_train: ROC-AUC score of the classifier on training data.
roc_test: ROC-AUC score of the classifier on testing data.
"""
x_train = np.transpose(mus_train)
x_test = np.transpose(mus_test)
clf = linear_model.LogisticRegression().fit(x_train, y_train)
y_pred_train = clf.predict_proba(x_train)
y_pred_test = clf.predict_proba(x_test)
mlb = preprocessing.MultiLabelBinarizer()
roc_train = metrics.roc_auc_score(
mlb.fit_transform(np.expand_dims(y_train, 1)), y_pred_train)
roc_test = metrics.roc_auc_score(
mlb.fit_transform(np.expand_dims(y_test, 1)), y_pred_test)
return roc_train, roc_test
def __init__(self, domain: Domain) -> None:
self.intent_enc = preprocessing.LabelEncoder()
intents = set(domain.acts_params + domain.dstc2_acts_sys)
self.intent_enc.fit([[x] for x in intents])
self.slot_enc = preprocessing.MultiLabelBinarizer()
slots = set(domain.requestable_slots + domain.system_requestable_slots)
self.slot_enc.fit([[x] for x in slots])
def cosine_model(m, classes=None, **cos_kws):
encode = pre.MultiLabelBinarizer(classes=classes)
tags = encode.fit_transform(m)
cos = cosine_similarity(tags.T.dot(tags))
cos -= np.diag(cos.diagonal())
return cos
def _encode_labels(self):
"""Encodes string or numeric y labels to integers using MultiLabelBinarizer"""
mlb = preprocessing.MultiLabelBinarizer()
mlb.fit(self.y)
mapping_dict = dict(
zip(list(range(0, len(mlb.classes_))), mlb.classes_))
return mlb, mapping_dict
def main():
y = [[2, 3, 4], [2], [0, 1, 3], [0, 1, 2, 3, 4], [0, 1, 2]]
y_transformed = preprocessing.MultiLabelBinarizer().fit_transform(y)
print('y_transformed =\n', y_transformed)
multiclass_example()
multioutput_classification_example()
multioutput_regression_example()
def generate_tags(df, hashtag_threshold=200):
mlb = preprocessing.MultiLabelBinarizer(sparse_output=True)
add_tag = np.vectorize(lambda x: f"hashtag_{x}")
hashtags = pd.DataFrame(mlb.fit_transform(df.hashtags).toarray(),
columns=add_tag(mlb.classes_))
hashtag_freq = hashtags.sum(axis=0).sort_values(ascending=False)
hashtags = hashtags.filter(hashtag_freq.iloc[:hashtag_threshold].index)
return hashtags
def trainfunctionclassifier(trees, sents, numproc):
"""Train a classifier to predict functions tags in trees."""
from sklearn import linear_model, multiclass, pipeline
from sklearn import preprocessing, feature_extraction
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import make_scorer, jaccard_similarity_score
vectorizer = pipeline.Pipeline([
('vectorizer', feature_extraction.DictVectorizer(sparse=True)),
('scaler', preprocessing.StandardScaler(
copy=False, with_mean=False))])
# PTB has no function tags on pretermintals, Negra/Tiger/Lassy do.
posfunc = any(functions(node) for tree in trees
for node in tree.subtrees()
if node and isinstance(node[0], int))
target = [functions(node) for tree in trees
for node in tree.subtrees()
if tree is not node and node
and (posfunc or isinstance(node[0], Tree))]
# PTB may have multiple tags (or 0) per node.
# Negra/Tiger/Lassy have exactly 1 tag for every node.
multi = any(len(a) > 1 for a in target)
if multi:
encoder = preprocessing.MultiLabelBinarizer()
else:
encoder = preprocessing.LabelEncoder()
target = [a[0] if a else '--' for a in target]
# binarize features (output is a sparse array)
trainfeats = vectorizer.fit_transform(functionfeatures(node, sent)
for tree, sent in zip(trees, sents)
for node in tree.subtrees()
if tree is not node
and node and (posfunc or isinstance(node[0], Tree)))
trainfuncs = encoder.fit_transform(target)
classifier = linear_model.SGDClassifier(
loss='hinge',
penalty='elasticnet',
n_iter=int(10 ** 6 / len(trees)))
alphas = [1e-1, 1e-2, 1e-3, 1e-4, 1e-5, 1e-6]
if multi:
classifier = multiclass.OneVsRestClassifier(
classifier, n_jobs=numproc or -1)
param_grid = dict(
estimator__alpha=alphas)
else:
param_grid = dict(alpha=alphas)
classifier = GridSearchCV(estimator=classifier, param_grid=param_grid,
scoring=make_scorer(jaccard_similarity_score))
# train classifier
classifier.fit(trainfeats, trainfuncs)
msg = ('trained classifier; grid search results:\n%s\n'
'multi=%r, posfunc=%r; best score on training set: %g %%\n'
'parameters: %r\nfunction tags: %s' % (
'\n'.join(str(a) for a in classifier.grid_scores_),
multi, posfunc, 100.0 * classifier.best_score_,
classifier.best_estimator_,
' '.join(str(a) for a in encoder.classes_)))
return (classifier, vectorizer, encoder, posfunc, multi), msg
def get_encoded_labels(self,
training_labels,
validation_labels,
test_labels,
multilabel=False):
if multilabel:
training_labels = [
label.strip().split(self.processing_params.multilabelsplitter)
for label in training_labels
]
validation_labels = [
label.strip().split(self.processing_params.multilabelsplitter)
for label in validation_labels
]
test_labels = [
label.strip().split(self.processing_params.multilabelsplitter)
for label in test_labels
]
total_labels = []
total_labels.extend([
label for jointlabel in training_labels for label in jointlabel
])
total_labels.extend([
label for jointlabel in validation_labels
for label in jointlabel
])
total_labels.extend(
[label for jointlabel in test_labels for label in jointlabel])
unique_labels = [np.unique(total_labels)]
self.label_transform = preprocessing.MultiLabelBinarizer()
else:
training_labels = [label.strip() for label in training_labels]
validation_labels = [label.strip() for label in validation_labels]
test_labels = [label.strip() for label in test_labels]
total_labels = training_labels + validation_labels + test_labels
unique_labels = np.unique(total_labels)
self.label_transform = preprocessing.LabelEncoder()
self.label_transform.fit(unique_labels)
training_labels = self.label_transform.transform(training_labels)
validation_labels = self.label_transform.transform(validation_labels)
test_labels = self.label_transform.transform(test_labels)
if multilabel:
return training_labels.astype(np.float32), \
validation_labels.astype(np.float32), \
test_labels.astype(np.float32), \
self.label_transform.classes_
else:
return training_labels.astype(np.int32), \
validation_labels.astype(np.int32), \
test_labels.astype(np.int32), \
self.label_transform.classes_
def fit(self, X, y):
# TODO Using 2 classes. Extra argument required if we want
# this to work with more than 2 classes
n_classes = 2
n_examples, n_features = X.shape
iterations = int(n_examples / self.batch_size)
total_iterations = self.n_epoch * iterations
# 1 column per value so will be easier later to make this work with multiple classes.
#y = y.astype(float)
y = preprocessing.MultiLabelBinarizer().fit_transform(
y.reshape(len(y), 1))
# Placeholders for input values.
self.x = tf.placeholder(tf.float64, [None, n_features], name='x')
self.y_ = tf.placeholder(tf.float64, [None, n_classes],
name='dataset-y')
# Variables for computed stuff, we need to initialise them now.
W = tf.Variable(tf.zeros([n_features, n_classes], dtype=np.float64),
name='weights')
b = tf.Variable(tf.zeros([n_classes], dtype=np.float64), name='bias')
# Predicted y.
self.z = tf.matmul(self.x, W) + b
self.y = tf.nn.softmax(self.z)
cross_entropy = -tf.reduce_sum(
self.y_ * tf.log(tf.clip_by_value(self.y, -1.0, 1.0)))
loss = tf.reduce_mean(cross_entropy)
# Calculate decay_rate.
learning_rate = self.calculate_decay_rate(total_iterations)
train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(
loss)
init = tf.initialize_all_variables()
self.sess.run(init)
for e in range(self.n_epoch):
for i in range(iterations):
offset = i * self.batch_size
it_end = offset + self.batch_size
if it_end > n_examples:
it_end = n_examples - 1
batch_xs = X[offset:it_end]
batch_ys = y[offset:it_end]
feed = {self.x: batch_xs, self.y_: batch_ys}
self.sess.run(train_step, feed_dict=feed)
def init_item_item_simmat():
t1 = time.time() * 1000
itemids = user_item_mat.columns
ITEM_ITEM_SIMMAT = pd.DataFrame(0.0,
index=itemids,
columns=itemids,
dtype='f8')
# 整理导演字段多标签二值化处理
movie_data.index = movie_data['movie_id']
movie_feature_map = pd.get_dummies(movie_data, columns=['director'])
print('movie_feature_map:', movie_feature_map.info(),
movie_feature_map.shape)
# 整理演员字段多标签二值化处理
L = pd.DataFrame(movie_data['actors'].str.split(
' / ', expand=True)).fillna('').values
import sklearn.preprocessing as sp
mlb = sp.MultiLabelBinarizer()
res = pd.DataFrame(mlb.fit_transform(L),
columns=mlb.classes_,
index=movie_data['movie_id'])
movie_feature_map2 = pd.concat([movie_feature_map, res], axis=1)
print('res:', res.info(), res.shape)
print('movie_feature_map2:', movie_feature_map2.info(),
movie_feature_map2.shape)
# 整理电影类型字段多标签二值化处理
G = pd.DataFrame(movie_data['genres'].str.split(
' / ', expand=True)).fillna('').values
import sklearn.preprocessing as sp
mlb = sp.MultiLabelBinarizer()
res = pd.DataFrame(mlb.fit_transform(G),
columns=mlb.classes_,
index=movie_data['movie_id'])
movie_feature_map3 = pd.concat([movie_feature_map2, res], axis=1)
# 计算相关性系数矩阵
movie_feature_map3.drop(movie_feature_map3.columns[np.arange(
len(MOVIE_DATA_COLUMNS))],
axis=1,
inplace=True)
print(movie_feature_map3.shape)
ITEM_ITEM_SIMMAT = movie_feature_map3.T.corr()
return ITEM_ITEM_SIMMAT
def featureEngineeringOfAmenities(df):
'''clean the amenities field and convert into list'''
df['amenities'] = df.apply(lambda x: parse_amenities(x.amenities), axis=1)
'''OHE the data of ammenities'''
'''we cannot use getdummies here as each row has a list of amenities.so we are using MultiLabelBinarizer '''
mlb = preprocessing.MultiLabelBinarizer()
amenities = pandas.DataFrame(mlb.fit_transform(df['amenities']),
columns=mlb.classes_,
index=df.index)
amenities = amenities.drop([
'translation missing: en.hosting_amenity_49',
'translation missing: en.hosting_amenity_50'
],
axis=1)
'''check corelation between amenities'''
cor_amn = pandas.DataFrame(amenities.corr())
for col in cor_amn.columns:
cor_amn.loc[col, col] = numpy.nan
high_cor = cor_amn.where(cor_amn.abs().gt(.8))
high_cor = high_cor.dropna(axis=1, how='all')
high_cor = high_cor.dropna(axis=0, how='all')
'''highly corelated with bathroom essentials. so remove them'''
amenities = amenities.drop(
['Bath towel', 'Bedroom comforts', 'Body soap', 'Toilet paper'],
axis=1)
'''highly corelated with cooking basics. so remove them'''
amenities = amenities.drop([
'Dishes and silverware', 'Oven', 'Refrigerator', 'Stove', 'Microwave'
],
axis=1)
'''highly corelated with self check in.so remove them'''
amenities = amenities.drop(['Lockbox'], axis=1)
'''highly corelated to toilet so remove'''
amenities = amenities.drop(['Wide clearance to shower'], axis=1)
'''delete original amenities column'''
df = df.drop(['amenities'], axis=1)
'''merge amenities with original data'''
df = pandas.DataFrame(pandas.concat([df, amenities], axis=1))
'''remove amenities which are most common or most uncommon'''
amenities_dist = dict()
unbalanced_amenities = list()
for i in amenities.columns:
freq = df[i].sum().item()
amenities_dist.update({i: freq})
if (freq < 1500 or freq > 70000):
unbalanced_amenities.append(i)
'''sort by most common'''
amenities_dist = dict(
sorted(amenities_dist.items(),
key=operator.itemgetter(1),
reverse=True))
'''get rid of amenities which have less than 3% of 0's or 1's in each column'''
df = df.drop(unbalanced_amenities, axis=1)
return (df)
def pandasToTensor(data, globalVocab):
data = shuffle(data)
# # Preprocessing data
# # retain only text that contain less that 70 tokens to avoid too much padding
data["token_size"] = data["text"].apply(lambda x: len(x.split(' ')))
data = data.loc[data['token_size'] < 70].copy()
# # sampling
# data = data.sample(n=50000);
# # construct vocab and indexing
# inputs = construct.ConstructVocab(data["text"].values.tolist())
# print(globalVocab.vocab[0:10])
input_tensor = [[globalVocab.word2idx[s] for s in es.split(' ')]
for es in data["text"].values.tolist()]
# examples of what is in the input tensors
# print(input_tensor[0:2])
# calculate the max_length of input tensor
max_length_inp = util.max_length(input_tensor)
# print(max_length_inp)
# inplace padding
input_tensor = [
util.pad_sequences(x, max_length_inp) for x in input_tensor
]
# print(input_tensor[0:2])
###Binarization
emotions = list(emotion_dict.values())
num_emotions = len(emotion_dict)
# print(emotions)
# binarizer
mlb = preprocessing.MultiLabelBinarizer(classes=emotions)
data_labels = [emos for emos in data[['emotions']].values]
# print(data_labels)
bin_emotions = mlb.fit_transform(data_labels)
target_tensor = np.array(bin_emotions.tolist())
# print(target_tensor[0:2])
# print(data[0:2])
get_emotion = lambda t: np.argmax(t)
get_emotion(target_tensor[0])
emotion_dict[get_emotion(target_tensor[0])]
return input_tensor, target_tensor
def load_scale_data(file_path, multilabeltf=False):
X, y = load_svmlight_file(file_path, multilabel=multilabeltf)
X = X.toarray()
# print X[:, 0]
# print X[:, 10]
# print X[:, 21]
# X = preprocessing.scale(X)
# min_max_scaler = preprocessing.MinMaxScaler()
# X = min_max_scaler.fit_transform(X_dentise)
if multilabeltf == True:
y = preprocessing.MultiLabelBinarizer().fit_transform(y)
return (X, y)
def prepare_one_hot_encoder(img_list, label_csv_path):
with open(img_list, "r") as f:
tmp_list = [i.strip() for i in f.readlines()]
y = []
meta_data = pd.read_csv(label_csv_path)
for pid in tmp_list:
labels = meta_data.loc[meta_data["Image Index"] == pid, "Finding Labels"]
tmp = labels.tolist()[0].split("|")
y.append(tmp)
encoder = preprocessing.MultiLabelBinarizer()
encoder.fit(y)
return encoder
def prepareTraining(X_train,
Y_train,
wl,
savgol=False,
msc=False,
rdp=False,
justbymoda=True,
plot=False):
"""
fonction qui permet de préparer les données d'entrainement pour la regression pls. En utilisant ou non
un pré-traitement, Binarise les classes en fonction du choix fait.
:param X_train: liste des reflectances associées à chaque échantillon
:param Y_train: liste de classes associées à chaque échantillon
:param wl: longueurs d'ondes
:param savgol: booléen si on utilise le pré-traitement Savitzky Golay
:param msc: booléen si on utilise le pré-traitement MSC
:param rdp: booléen si on utilise le pré-traitement RDP
:param justbymoda: booléen on sépare les classes par moda, ou par moda,feuille,das
:param plot: booléen si on veut un affichage ou non
:return: une nouvelle liste X de reflectances, une nouvelle liste Y de classes (binaire), la référence ref des
données passées par MSC (None si msc=False), les longueurs d'ondes wlrdp passées par RDP (=wl si rdp=False)
"""
#print("prepareTraining: "+str(len(X_train))+" "+str(len(X_train[0])))
X_train = X_train.astype('float32')
if savgol is True:
X_train = pre_traitement.lissageSavitzky(X_train, wl)
ref = None
if msc is True:
X_train, ref = pre_traitement.compute_msc(X_train, wl, plot=plot)
wlrdp = wl
if rdp is True:
wlrdp, X_train = pre_traitement.computeRdpForData(wl,
X_train,
Y_train,
justbymoda,
plot=plot)
X = getDatatoDataframe(X_train)
#binarisation des classes
lb = preprocessing.MultiLabelBinarizer()
if justbymoda is True:
lb = preprocessing.LabelBinarizer()
Y = lb.fit_transform(Y_train)
oneShotDictionary = {}
for i in range(0, Y.shape[0]):
# print("Binarized labels training: "+str(Y[i])+" - "+str(Y_train[i])+" -> "+str(str(Y[i]) in oneShotDictionary))
if (str(Y[i]) in oneShotDictionary) is False:
oneShotDictionary[str(Y[i])] = Y_train[i]
return oneShotDictionary, X, Y, ref, wlrdp
def _labels_encoder(self):
"""
prepare labels encoder from string to digits
"""
pd_meta = self._load_fold_list(fold=1, data_split=self.DataSplit.train)
labels_list = pd_meta[self.MetaCol.LAB].astype(str)
if self.is_multilabel:
le = sk_proc.MultiLabelBinarizer()
labels_list = labels_list.str.split(self.LABEL_SEPARATOR)
else:
le = sk_proc.LabelEncoder()
le.fit(labels_list)
return le
def ro_gt(start_time, end_time, feature_length):
start_time=dt.strptime(start_time, "%Y-%m-%d");
end_time=dt.strptime(end_time, "%Y-%m-%d");
fl = str(feature_length) + "s";
timestamps=pd.date_range(start_time, end_time, freq=fl);
dred_df = pd.DataFrame.from_csv('../dataset/Occupancy_data_split.csv');
# need to use different groupby, that can adjust based on feature length
dred_fl = dred_df.groupby(pd.TimeGrouper(fl))[u'room'].apply(set).apply(list);
dred_fl = dred_fl.apply(lambda x: float('NaN') if len(x)==0 else x).dropna();
mlb = preprocessing.MultiLabelBinarizer();
dred_bin = mlb.fit_transform(dred_fl);
dred_bin_df = pd.DataFrame(data=dred_bin, columns=list(mlb.classes_), index=dred_fl.index);
return dred_bin_df.loc[timestamps].dropna();
