def subsample_to_file(svm_file, out_dir, out_name, multilabel=False,
row_ratio=0.5, col_ratio=0.3, random_state=12):
"""
Example:
'''python
# run the following command in the current directory will create a
# `tmp` folder, if not already exists, and generate a file called
# `a9a_sub` from the original file `./data/a9a`. Both files are
# in libsvm format.
subsample_to_file("./data/a9a", "./tmp", "a9a_sub")
# read the subsampled file and make sure its number of rows is half of
# that of a9a and its number of column is roughly third of a9a (123)
X, y = load_svmlight_file('./tmp/a9a_sub')
assert X.shape == (16280, 36)
'''
"""
assert 1 >= row_ratio > 0, \
"Row ratio {row_ratio} must be (0, 1]" \
.format(**locals())
assert 1 >= col_ratio > 0, \
"Col ratio {col_ratio} must be (0, 1]" \
.format(**locals())
X, y = load_svmlight_file(svm_file, multilabel=multilabel)
n, m = X.shape
subn = int(n*row_ratio)
subm = int(m*col_ratio)
rst = np.random.RandomState(random_state)
ridx = rst.choice(n, subn, replace=False)
cidx = rst.choice(m, subm, replace=False)
mkdir_p(out_dir)
out_file = os.path.join(out_dir, out_name)
dump_svmlight_file(X[ridx,:][:,cidx], y[ridx],
out_file, multilabel=multilabel)
def save_libfm(X_sprs_mat, y_array, f):
print("Save LibFM Format")
dump_svmlight_file(X_sprs_mat, y_array, f)
return
def test_load_with_offsets(sparsity, n_samples, n_features):
rng = np.random.RandomState(0)
X = rng.uniform(low=0.0, high=1.0, size=(n_samples, n_features))
if sparsity:
X[X < sparsity] = 0.0
X = sp.csr_matrix(X)
y = rng.randint(low=0, high=2, size=n_samples)
f = BytesIO()
dump_svmlight_file(X, y, f)
f.seek(0)
size = len(f.getvalue())
# put some marks that are likely to happen anywhere in a row
mark_0 = 0
mark_1 = size // 3
length_0 = mark_1 - mark_0
mark_2 = 4 * size // 5
length_1 = mark_2 - mark_1
# load the original sparse matrix into 3 independent CSR matrices
X_0, y_0 = load_svmlight_file(f, n_features=n_features,
offset=mark_0, length=length_0)
X_1, y_1 = load_svmlight_file(f, n_features=n_features,
offset=mark_1, length=length_1)
X_2, y_2 = load_svmlight_file(f, n_features=n_features,
offset=mark_2)
y_concat = np.concatenate([y_0, y_1, y_2])
X_concat = sp.vstack([X_0, X_1, X_2])
assert_array_almost_equal(y, y_concat)
assert_array_almost_equal(X.toarray(), X_concat.toarray())
def test_dump():
Xs, y = load_svmlight_file(datafile)
Xd = Xs.toarray()
for X in (Xs, Xd):
for zero_based in (True, False):
for dtype in [np.float32, np.float64]:
f = BytesIO()
dump_svmlight_file(X.astype(dtype), y, f, zero_based=zero_based)
f.seek(0)
comment = f.readline()
assert_in("scikit-learn %s" % sklearn.__version__, comment)
comment = f.readline()
assert_in(["one", "zero"][zero_based] + "-based", comment)
X2, y2 = load_svmlight_file(f, dtype=dtype, zero_based=zero_based)
assert_equal(X2.dtype, dtype)
if dtype == np.float32:
assert_array_almost_equal(
# allow a rounding error at the last decimal place
Xd.astype(dtype),
X2.toarray(),
4,
)
else:
assert_array_almost_equal(
# allow a rounding error at the last decimal place
Xd.astype(dtype),
X2.toarray(),
15,
)
assert_array_equal(y, y2)
def batch_fit(self, Xs, ys, dump=True):
qids = [np.array([i] * len(ys[i])) for i in range(len(ys))]
print "dumping data to Xtrain.data"
if dump:
dump_svmlight_file(
np.concatenate(Xs), np.concatenate(ys), "Xtrain.data", zero_based=False, query_id=np.concatenate(qids)
)
print "now learning"
print call(
[
self.path + "svm_hmm_learn",
"-c",
"%d" % self.C,
"--t",
"%d" % self.t,
"--e",
"%d" % self.e,
"Xtrain.data",
"svmhmm-model.dat",
]
)
return self
def fit(self, X, Y):
self.labels=list(set(Y))
if len(self.labels) > 2 :
self.multiclass=True
#print 'multiclass'
else:
self.multiclass=False
self.train_fname =self.base_str +'-svmcmd-train' + '.dat'
self.model_fname =self.train_fname + '.model'
dump_svmlight_file(X,Y,self.train_fname ,zero_based=False)
if self.multiclass:
command_line=path_to_train_program+'gtsvm_initialize {0} -f {1} -o {2} -m 1 '.format(self.param_str, self.train_fname , self.model_fname )
else:
command_line=path_to_train_program+'gtsvm_initialize -f {1} -o {2} {0}'.format(self.param_str, self.train_fname , self.model_fname )
args = shlex.split(command_line)
p = subprocess.Popen(args)
p.wait()
command_line=path_to_train_program+'gtsvm_optimize -i {0} -o {1} -e {2} -n {3}'.format(self.model_fname,self.model_fname,self.tol,self.max_iter)
args = shlex.split(command_line)
p = subprocess.Popen(args,stderr=subprocess.PIPE)
p.wait()
opt_err_str=p.stderr.read() ##gtsvm is too buggy
if len(opt_err_str) < 1:
command_line=path_to_train_program+'gtsvm_shrink -i {0} -o {1}'.format(self.model_fname,self.model_fname)
args = shlex.split(command_line)
p = subprocess.Popen(args)
p.wait()
self.train_fail=False
else :
self.train_fail=True
return self
def predict(self, X):
if isinstance(X,list):
self.test_n_sample=len(X)
else:
self.test_n_sample=X.shape[0]
Y=[1]*self.test_n_sample
self.test_fname =self.base_str +'-svmcmd-test' + '.dat'
self.predict_fname =self.base_str +'-svmcmd-predict' + '.dat'
dump_svmlight_file(X,Y,self.test_fname ,zero_based=False)
command_line=path_to_train_program+'gtsvm_classify -f {0} -i {1} -o {2}'.format(self.test_fname , self.model_fname, self.predict_fname )
args = shlex.split(command_line)
p = subprocess.Popen(args)
p.wait()
if self.train_fail:
return [max(self.labels)+1]*self.test_n_sample
if self.multiclass :
f = open(self.predict_fname, 'rb')
self.predicted_weight = map(lambda row: map(float,row), list(csv.reader(f)))
f.close()
Y_predict=map(np.argmax, self.predicted_weight)
else :
self.predicted_weight = np.loadtxt( self.predict_fname)
Y_predict=map(int,map(round,self.predicted_weight))
return Y_predict
def generate_weekday_newbuyer_exposure(df):
"""
加入新客数，曝光数
"""
X = df[['uv_0612_0618', 'uv_weekday', 'uv_weekend', 'no_subsidy_exposure', 'newbuyer_6_18']]
y = df.uv_0626_0702
dump_svmlight_file(X, y, './uv_weekday_weekend_newbuyer_exposure_without_outliers.dat')
def generate_week(df):
"""
生成1维特征
"""
X = df[['uv_0612_0618']]
y = df.uv_0626_0702
dump_svmlight_file(X, y, './uv_week.dat')
def executa_extracao_n(base_treino, metodo, n=1):
inicio = time()
lista_imagens = arq.busca_arquivos(base_treino, "*.png")
n_imgs_treino = len(lista_imagens)
for lado in range(8,n+1,4):
atributos = []
rotulos = []
arq_treino = base_treino + "base_PFTAS_"+str(lado)+"x"+str(lado)+".svm"
## INICIO DO PROCESSO DE EXTRACAO DE ATRIBUTOS
for arq_imagem in lista_imagens:
print("Arquivo: " + arq_imagem)
imagem = mh.imread(arq_imagem)
if (imagem != None):
classe, _ = ex.classe_arquivo(arq_imagem)
print("executa_extracao_n - shape imagem:" + str(imagem.shape))
# Extrai os atributos e gera os arquivos dos patches da base de treino
atrs,rots = extrai_pftas_patches_n(imagem, classe, lado)
atributos += atrs
rotulos += rots
dump_svmlight_file(atributos, rotulos, arq_treino)
log("Extraidos atributos da base " + base_treino + " utilizando " + metodo + "\n para " + str(n_imgs_treino) + "imagens")
# Exibe o tempo de execução
log(str(time()-inicio) + "EXTRAÇÃO")
def generate_weekday_weekend(df):
"""
生成3维特征
"""
X = df[['uv_0612_0618', 'uv_weekday', 'uv_weekend']]
y = df.uv_0626_0702
dump_svmlight_file(X, y, './uv_weekday_weekend.dat')
def test_dump():
Xs, y = load_svmlight_file(datafile)
Xd = Xs.toarray()
for X in (Xs, Xd):
for zero_based in (True, False):
for dtype in [np.float32, np.float64]:
f = BytesIO()
# we need to pass a comment to get the version info in;
# LibSVM doesn't grok comments so they're not put in by
# default anymore.
dump_svmlight_file(X.astype(dtype), y, f, comment="test",
zero_based=zero_based)
f.seek(0)
comment = f.readline()
assert_in("scikit-learn %s" % sklearn.__version__, comment)
comment = f.readline()
assert_in(["one", "zero"][zero_based] + "-based", comment)
X2, y2 = load_svmlight_file(f, dtype=dtype,
zero_based=zero_based)
assert_equal(X2.dtype, dtype)
if dtype == np.float32:
assert_array_almost_equal(
# allow a rounding error at the last decimal place
Xd.astype(dtype), X2.toarray(), 4)
else:
assert_array_almost_equal(
# allow a rounding error at the last decimal place
Xd.astype(dtype), X2.toarray(), 15)
assert_array_equal(y, y2)
def save_all_data_in_svmlight_format(self,
file_path,
extraction_method,
label_type):
label_list, feature_vector_list = self.extract_all_data(extraction_method, label_type)
with open(file_path, 'wb') as f:
datasets.dump_svmlight_file(feature_vector_list, label_list, f)
def dump_svmlight(X_matrix, Y, feature_names, output_filename, feature_id_offset = 0):
dump_svmlight_file(X_matrix, Y, output_filename)
contents = None
with open(output_filename) as output_file:
contents = '#' + ' '.join(feature_names) + '\n' + ''.join(output_file.readlines())
with open(output_filename, 'w') as output_file:
output_file.write(contents)
def data_dump(self, f, X_train, X_test, y_train, y_test):
from sklearn.datasets import dump_svmlight_file
ddd = dict()
new_y_train = []
last = 0
for yy in y_train:
if yy in ddd:
yy = (ddd[yy])
else:
ddd[yy] = last
yy = last
last += 1
new_y_train.append(yy)
dump_svmlight_file(X_train, new_y_train, f + ".svmlight.train")
new_y_test = []
for yy in y_test:
if yy in ddd:
yy = (ddd[yy])
else:
ddd[yy] = last
yy = last
last += 1
new_y_test.append(yy)
dump_svmlight_file(X_test, new_y_test, f + ".svmlight.test")
def load_training_data(file_location=str, load_from_database=False, limit=int(1000), clean_dataset=True):
"""
If ```load_from_database``` is True, retrieves and stores data from database to file.
Arguments:
file_location (str): Path + filename of libsvm file to save/load (e.g. 'training_data')
load_from_database (bool): Should data be retrieved from database?
limit (int): Amount of records to retrieve from database (default=1000)
clean_dataset (bool): Should questions be cleaned (e.g. remove code samples, hexadecimals, numbers, etc)?
Returns:
(pandas.DataFrame.from_csv, sklearn.datasets.load_svmlight_file):
Tuple containing a pandas.DataFrame (all data retrieved from database) and
tuple with training data (load_svmlight_file)
See:
| ```MySQLDatabase().retrieve_training_data```
| ```pandas.DataFrame.to_csv```
| ```pandas.DataFrame.from_csv```
| ```sklearn.datasets.dump_svmlight_file```
| ```sklearn.datasets.load_svmlight_file```
"""
svm_file = file_location + ".dat"
csv_file = file_location + ".csv"
if load_from_database:
comment = u"label: (-1: Bad question, +1: Good question); features: (term_id, frequency)"
MySQLDatabase().set_vote_value_params()
data = MySQLDatabase().retrieve_training_data(limit, clean_dataset)
# create a term-document matrix
vectorizer = CountVectorizer(analyzer='word', min_df=0.01, stop_words="english")
td_matrix = vectorizer.fit_transform(data.get(QUESTION_TEXT_KEY))
data.to_csv(csv_file)
dump_svmlight_file(td_matrix, data[CLASS_LABEL_KEY], f=svm_file, comment=comment)
return DataFrame.from_csv(csv_file), load_svmlight_file(svm_file)
def test_dump_comment():
X, y = load_svmlight_file(datafile)
X = X.toarray()
f = BytesIO()
ascii_comment = "This is a comment\nspanning multiple lines."
dump_svmlight_file(X, y, f, comment=ascii_comment, zero_based=False)
f.seek(0)
X2, y2 = load_svmlight_file(f, zero_based=False)
assert_array_almost_equal(X, X2.toarray())
assert_array_equal(y, y2)
# XXX we have to update this to support Python 3.x
utf8_comment = "It is true that\n\xc2\xbd\xc2\xb2 = \xc2\xbc"
f = BytesIO()
assert_raises(UnicodeDecodeError, dump_svmlight_file, X, y, f, comment=utf8_comment)
unicode_comment = utf8_comment.decode("utf-8")
f = BytesIO()
dump_svmlight_file(X, y, f, comment=unicode_comment, zero_based=False)
f.seek(0)
X2, y2 = load_svmlight_file(f, zero_based=False)
assert_array_almost_equal(X, X2.toarray())
assert_array_equal(y, y2)
f = BytesIO()
assert_raises(ValueError, dump_svmlight_file, X, y, f, comment="I've got a \0.")
def test_load_with_long_qid():
# load svmfile with longint qid attribute
data = b("""
1 qid:0 0:1 1:2 2:3
0 qid:72048431380967004 0:1440446648 1:72048431380967004 2:236784985
0 qid:-9223372036854775807 0:1440446648 1:72048431380967004 2:236784985
3 qid:9223372036854775807 0:1440446648 1:72048431380967004 2:236784985""")
X, y, qid = load_svmlight_file(BytesIO(data), query_id=True)
true_X = [[1, 2, 3],
[1440446648, 72048431380967004, 236784985],
[1440446648, 72048431380967004, 236784985],
[1440446648, 72048431380967004, 236784985]]
true_y = [1, 0, 0, 3]
trueQID = [0, 72048431380967004, -9223372036854775807, 9223372036854775807]
assert_array_equal(y, true_y)
assert_array_equal(X.toarray(), true_X)
assert_array_equal(qid, trueQID)
f = BytesIO()
dump_svmlight_file(X, y, f, query_id=qid, zero_based=True)
f.seek(0)
X, y, qid = load_svmlight_file(f, query_id=True, zero_based=True)
assert_array_equal(y, true_y)
assert_array_equal(X.toarray(), true_X)
assert_array_equal(qid, trueQID)
f.seek(0)
X, y = load_svmlight_file(f, query_id=False, zero_based=True)
assert_array_equal(y, true_y)
assert_array_equal(X.toarray(), true_X)
def test_dump_concise():
one = 1
two = 2.1
three = 3.01
exact = 1.000000000000001
# loses the last decimal place
almost = 1.0000000000000001
X = [[one, two, three, exact, almost],
[1e9, 2e18, 3e27, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]]
y = [one, two, three, exact, almost]
f = BytesIO()
dump_svmlight_file(X, y, f)
f.seek(0)
# make sure it's using the most concise format possible
assert_equal(f.readline(),
b("1 0:1 1:2.1 2:3.01 3:1.000000000000001 4:1\n"))
assert_equal(f.readline(), b("2.1 0:1000000000 1:2e+18 2:3e+27\n"))
assert_equal(f.readline(), b("3.01 \n"))
assert_equal(f.readline(), b("1.000000000000001 \n"))
assert_equal(f.readline(), b("1 \n"))
f.seek(0)
# make sure it's correct too :)
X2, y2 = load_svmlight_file(f)
assert_array_almost_equal(X, X2.toarray())
assert_array_equal(y, y2)
def create_train_test(n_samples, doc2vec, save_svmlight=True):
print "Creating train & test sets..."
# Create labelled data arrays.
data = np.zeros((n_samples, doc2vec.size))
labels = np.zeros(n_samples)
for i in range(n_samples / 2):
prefix_train_pos = 'TRAIN_POS_' + str(i)
prefix_train_neg = 'TRAIN_NEG_' + str(i)
data[i] = doc2vec.model.docvecs[prefix_train_pos]
data[n_samples / 2 + i] = doc2vec.model.docvecs[prefix_train_neg]
labels[i] = 1
# Split in train and validation arrays.
train, test, train_labels, test_labels = train_test_split(
data, labels, test_size=0.3, random_state=42)
if save_svmlight:
current_path = os.path.abspath(
os.path.join(os.getcwd(), os.pardir))
dump_svmlight_file(train, train_labels, current_path + "/Data/Processed/TrainSet.svm")
dump_svmlight_file(test, test_labels, current_path + "/Data/Processed/TestSet.svm")
return train, test, train_labels, test_labels
def pair_vectors(pairs, features, words, output_path):
vectorizer = DictVectorizer()
vectors = vectorizer.fit_transform(x[1] for x in features)
vector_map = {word:vector for word, vector in
itertools.izip((x[0].split('/')[0] for x in features),
vectors)}
# Positive examples
positive = []
record = []
for specific, general in pairs:
positive.append(vector_map[general] - vector_map[specific])
record.append( (specific, general, 1) )
pair_set = set([tuple(x) for x in pairs])
non_positive = []
for i in range(len(positive)):
first = second = None
while first == second or (first, second) in pair_set:
first = words[random.randint(len(words))]
second = words[random.randint(len(words))]
non_positive.append(vector_map[second] - vector_map[first])
record.append( (first, second, 0) )
data = vstack(positive + non_positive)
target = [1]*len(positive) + [0]*len(non_positive)
# Save dataset
with open(os.path.join(output_path,'wn-noun-dependencies.mat'), 'wb') as data_file:
dump_svmlight_file(data, target, data_file)
with open(os.path.join(output_path,'wn-noun-dependencies.json'), 'w') as record_file:
json.dump(record, record_file)
def predict(self,X) :
# write test file to specific format
dump_svmlight_file(X,np.zeros(X.shape[0]),self._test_file_name,zero_based=True)
# call _exectute_prediction
self._execute_prediction(self._test_file_name)
# import the output of the script and return it
predictions = pd.read_csv(self._temp_pred_file_name,header = None)
return predictions
def download_mnist():
training, test = fetch_mnist(data_home=dataset_dir)
X, y = training
datasets.dump_svmlight_file(X, y, join(dataset_dir, "mnist"))
X, y = test
datasets.dump_svmlight_file(X, y, join(dataset_dir, "mnist.t"))
def save_svmlight(x, y, path):
LOG.debug("saving svmlight to %s", path)
ensure_exist(path)
_, n = x.shape
with open(os.path.join(path, "nfeature.txt"), "wb") as f:
f.write(str(n))
dump_svmlight_file(x, y, os.path.join(path, "data"), zero_based=False)
def train_predict(train_file, test_file, predict_valid_file, predict_test_file,
n_iter=100, dim=4, lrate=.1, n_fold=5):
feature_name = os.path.basename(train_file)[:-8]
logging.basicConfig(format='%(asctime)s %(levelname)s %(message)s',
level=logging.DEBUG, filename='libfm_{}_{}_{}_{}.log'.format(
n_iter, dim, lrate,
feature_name
))
logging.info('Loading training data')
X, y = load_svmlight_file(train_file)
cv = StratifiedKFold(y, n_folds=n_fold, shuffle=True, random_state=2015)
logging.info('Cross validation...')
p = np.zeros_like(y)
lloss = 0.
for i_trn, i_val in cv:
now = datetime.now().strftime('%Y%m%d-%H%M%S')
valid_train_file = '/tmp/libfm_train_{}_{}.sps'.format(feature_name, now)
valid_test_file = '/tmp/libfm_valid_{}_{}.sps'.format(feature_name, now)
valid_predict_file = '/tmp/libfm_predict_{}_{}.sps'.format(feature_name, now)
dump_svmlight_file(X[i_trn], y[i_trn], valid_train_file,
zero_based=False)
dump_svmlight_file(X[i_val], y[i_val], valid_test_file,
zero_based=False)
subprocess.call(["libFM",
"-task", "c",
'-dim', '1,1,{}'.format(dim),
'-init_stdev', str(lrate),
'-iter', str(n_iter),
'-train', valid_train_file,
'-test', valid_test_file,
'-out', valid_predict_file])
p[i_val] = np.loadtxt(valid_predict_file)
lloss += log_loss(y[i_val], p[i_val])
os.remove(valid_train_file)
os.remove(valid_test_file)
os.remove(valid_predict_file)
logging.info('Log Loss = {:.4f}'.format(lloss / n_fold))
np.savetxt(predict_valid_file, p, fmt='%.6f')
logging.info('Retraining with 100% data...')
subprocess.call(["libFM",
"-task", "c",
'-dim', '1,1,{}'.format(dim),
'-init_stdev', str(lrate),
'-iter', str(n_iter),
'-train', train_file,
'-test', test_file,
'-out', predict_test_file])
def get_train_and_test_spaese_matrix():
Y = pd.read_csv('data/train_Y.csv', index_col='user_id')['type']
train_X = combine_all_behavior()
# dump_svmlight_file(train_X,Y,'data/train_metrix')
dump_svmlight_file(train_X,Y,'data/train_metrix_3')
test_X = combine_all_behavior(is_train=False)
test_Y = [0]*(test_X.shape[0])
# dump_svmlight_file(test_X,test_Y,'data/test_metrix')
dump_svmlight_file(test_X,test_Y,'data/test_metrix_3')
def test_dump():
X_sparse, y_dense = load_svmlight_file(datafile)
X_dense = X_sparse.toarray()
y_sparse = sp.csr_matrix(y_dense)
# slicing a csr_matrix can unsort its .indices, so test that we sort
# those correctly
X_sliced = X_sparse[np.arange(X_sparse.shape[0])]
y_sliced = y_sparse[np.arange(y_sparse.shape[0])]
for X in (X_sparse, X_dense, X_sliced):
for y in (y_sparse, y_dense, y_sliced):
for zero_based in (True, False):
for dtype in [np.float32, np.float64, np.int32]:
f = BytesIO()
# we need to pass a comment to get the version info in;
# LibSVM doesn't grok comments so they're not put in by
# default anymore.
if (sp.issparse(y) and y.shape[0] == 1):
# make sure y's shape is: (n_samples, n_labels)
# when it is sparse
y = y.T
dump_svmlight_file(X.astype(dtype), y, f, comment="test",
zero_based=zero_based)
f.seek(0)
comment = f.readline()
comment = str(comment, "utf-8")
assert_in("scikit-learn %s" % sklearn.__version__, comment)
comment = f.readline()
comment = str(comment, "utf-8")
assert_in(["one", "zero"][zero_based] + "-based", comment)
X2, y2 = load_svmlight_file(f, dtype=dtype,
zero_based=zero_based)
assert_equal(X2.dtype, dtype)
assert_array_equal(X2.sorted_indices().indices, X2.indices)
X2_dense = X2.toarray()
if dtype == np.float32:
# allow a rounding error at the last decimal place
assert_array_almost_equal(
X_dense.astype(dtype), X2_dense, 4)
assert_array_almost_equal(
y_dense.astype(dtype), y2, 4)
else:
# allow a rounding error at the last decimal place
assert_array_almost_equal(
X_dense.astype(dtype), X2_dense, 15)
assert_array_almost_equal(
y_dense.astype(dtype), y2, 15)
def project(file_name, dimensions):
data = load_svmlight_file(file_name)
projector = SparseRandomProjection(dimensions, 1/3.0,
dense_output=True)
projected = projector.fit_transform(data[0])
new_file_name = file_name[:-4] + '-' + str(dimensions) + '.mat'
new_file = open(new_file_name, 'wb')
dump_svmlight_file(projected, data[1], new_file)
def save_output(self, X, epoch=None):
# write output to file
if epoch is not None:
fname_out = self.conf.fname_out.replace('%e', str(epoch).zfill(5))
else:
fname_out = self.conf.fname_out.replace('%e', 'final')
if self.conf.verbosity > 1:
print "Saving output to", fname_out, "..."
dump_svmlight_file(X, self.tl, fname_out)
def glimpse_to_svmlight(input_file, train_file, test_file):
with open(input_file) as fh:
exp = pickle.load(fh)
ftrs = ExtractFeatures(Layer.C2, exp.extractor.activation)
trng = GetTrainingSet(exp)
dump_svmlight_file(ftrs[trng], exp.corpus.labels[trng] + 1, train_file, zero_based=False)
dump_svmlight_file(ftrs[~trng], exp.corpus.labels[~trng] + 1, test_file, zero_based=False)
print "Categories"
print "----------"
print "\n".join("%d - %s" % (index+1,name) for (index,name) in enumerate(exp.corpus.class_names))
folds = preprocess.create_folds(X, y, queries, 5)
fold_number = 1
C_array = [0.1, 0.01, 0.001]
# model_handler = mh.models_handler(C_array)
validated = set()
scores = {}
models = {}
for train, test in folds:
evaluator.empty_validation_files()
validated, validation_set, train_set = preprocess.create_validation_set(
5, validated, set(train), number_of_queries, queries)
train_file = "train" + str(fold_number) + ".txt"
run_command("rm " + train_file)
dump_svmlight_file(X[train],
y[train],
train_file,
query_id=queries[train],
zero_based=False)
for C in C_array:
model_file = learn_svm(C, train_file, fold_number)
weights = recover_model(model_file)
svm = s.svm_sgd(C)
svm.w = weights
score_file = svm.predict(X, queries, validation_set, evaluator,
True)
score = evaluator.run_trec_eval(score_file, qrels_file)
scores[svm.C] = score
models[svm.C] = svm
max_C = max(scores.items(), key=operator.itemgetter(1))[0]
chosen_model = models[max_C]
chosen_model.predict(X, queries, test, evaluator)
def random(data, d_sub, name, what, array, R, n_label):
np.set_printoptions(threshold=np.inf, suppress=True)
data = scipy.sparse.coo_matrix.tocsr(data)
label = data[:, 0]
data = sklearn.preprocessing.maxabs_scale(data[:, 1:])
n_sample, d_feature = np.shape(data)
a = math.sqrt(float(d_feature / d_sub)) * data
#
# pca = PCA(n_components=d_sub).fit_transform(a)
#
# label = (np.asarray(label)).reshape(-1)
# dump_svmlight_file(pca, label, 'other_method/kmeans_linear/%s/%s_pca' % (name, what), zero_based=False)
train2 = a[:, R]
dump_svmlight_file(train2,
label,
'other_method/kmeans_linear/%s/%s_random' %
(name, what),
zero_based=False)
# func = KMeans(n_clusters=d_sub)
# transform = a.T
# kmeans = func.fit(transform)
# klabel = kmeans.labels_
# R_means = []
# for i in range(d_sub):
# temp1 = []
# temp2 = []
# for idx, val in enumerate(klabel):
# if val == i:
# temp1.append(idx)
# distance = func.transform(transform[idx])
# temp2.append(distance[0, val])
#
# idx_temp = temp2.index(min(temp2))
# R_means.append(temp1[idx_temp])
# R_means = sorted(R_means)
# train2 = a[:,R_means]
# label = (np.asarray(label)).reshape(-1)
# dump_svmlight_file(train2, label, 'other_method/kmeans_linear/%s/%s_means' % (name, what),
# zero_based=False)
# b = a
# transform = b.T
# var = np.var(transform, axis=1)
# topn = np.argsort(var, axis=0)
# topn = np.reshape(topn, (1, -1))
# topn = np.asarray(topn)
# R_variace = topn[0, d_feature - d_sub:]
# R_variace = sorted(R_variace)
# R_variace = np.asarray(R_variace)
# train2 = a[:,R_variace]
# label = (np.asarray(label)).reshape(-1)
# dump_svmlight_file(train2, label, 'other_method/kmeans_linear/%s/%s_variance' % (name, what),
# zero_based=False)
b = a[:n_label]
transform = b.T
norm = linalg.norm(transform, axis=1)
# print(norm)
topn = np.argsort(norm, axis=0)
# print(topn)
topn = np.reshape(topn, (1, -1))
topn = np.asarray(topn)
R_norm = topn[0, d_feature - d_sub:]
R_norm = sorted(R_norm)
R_norm = np.asarray(R_norm)
train2 = a[:, R_norm]
# exit()
dump_svmlight_file(train2,
label,
'other_method/kmeans_linear/%s/%s_norm' % (name, what),
zero_based=False)
def save_as_svmlight(M, yid, fname):
dump_svmlight_file(M[:, [x for x in range(M.shape[1]) if x != yid]],
M[:, yid], fname)
dev_text_token_list = []
for text in dev_review_list:
dev_text_token_list.append(Counter(set(text)))
test_text_token_list = []
for text in test_review_list:
test_text_token_list.append(Counter(set(text)))
print('Save Data')
#Save csr_matrix data into libsvm
train_review_matrix = review_to_csr_matrix(train_text_token_list,
feature_list, feature_num)
train_stars_matrix = stars_to_csr_matrix(train_stars_list, class_num)
dump_svmlight_file(train_review_matrix,
train_stars_matrix,
str(method + '_train.libsvm'),
multilabel=True)
print('Data saved: {}'.format(str(method + '_train.libsvm')))
eval_review_matrix = review_to_csr_matrix(eval_text_token_list,
feature_list, feature_num)
eval_stars_matrix = stars_to_csr_matrix(eval_stars_list, class_num)
dump_svmlight_file(eval_review_matrix,
eval_stars_matrix,
str(method + '_eval.libsvm'),
multilabel=True)
print('Data saved: {}'.format(str(method + '_eval.libsvm')))
dev_review_matrix = review_to_csr_matrix(dev_text_token_list, feature_list,
feature_num)
dev_stars_matrix = stars_to_csr_matrix(dev_stars_list, class_num)
# -*- coding: utf-8 -*-
"""
Created on Sun Sep 9 00:18:22 2018
@author: Nitin
"""
"""
from sklearn.datasets import load_svmlight_file, dump_svmlight_file
file = "../data/usps/usps-train.dat"
X,y = load_svmlight_file(file)
dump_svmlight_file(X, y, file, zero_based=True, comment=None, query_id=None, multilabel=False)
file = "../data/usps/usps-test.dat"
X,y = load_svmlight_file(file)
dump_svmlight_file(X, y, file, zero_based=True, comment=None, query_id=None, multilabel=False)
for i in range(10):
file = "../data/usps/t_{}.dat".format(i)
X,y = load_svmlight_file(file)
dump_svmlight_file(X, y, file, zero_based=True, comment=None, query_id=None, multilabel=False)
"""
# -*- coding: utf-8 -*-
"""
Created on Fri Feb 23 23:20:58 2018
File to load and process the USPS data into a binary classification format.
@author: Nitin
"""
import numpy as np
#!/usr/bin/python
# -*- coding: utf-8 -*-
from sklearn.datasets import dump_svmlight_file
import pandas as pd
import numpy as np
df = pd.read_csv("transfered.csv")
y_data = np.array(df['is_malware'])
del df['is_malware']
del df['sha1']
X_data = np.array(df)
dump_svmlight_file(X_data,
y_data,
'apk_libsvm.dat',
zero_based=False,
multilabel=False)
model_file = os.path.join(data_path, "sofml.model")
training_file = os.path.join(data_path, "train_data.dat")
training2_file = os.path.join(data_path, "train2_data.dat")
test_file = os.path.join(data_path, "test_data.dat")
pred_train2_file = os.path.join(data_path, "pred_train2.csv")
pred_test_file = os.path.join(data_path, "pred_test.csv")
# write out traindata and testdata to svmlight format
print "writing out files"
ntrain1_label = train1_label.copy()
ntrain1_label.values[np.where(ntrain1_label == 0)] = -1
ntrain2_label = train2_label.copy()
ntrain2_label.values[np.where(ntrain2_label == 0)] = -1
dump_svmlight_file(train1_data_norm,
ntrain1_label,
training_file,
zero_based=False)
dump_svmlight_file(train2_data_norm,
ntrain2_label,
training2_file,
zero_based=False)
dump_svmlight_file(test_data_norm,
np.zeros((test_data_norm.shape[0], )),
test_file,
zero_based=False)
# train
#print "training sofia"
call(
sofiaml_path +
" --learner_type sgd-svm --loop_type roc --prediction_type logistic --iterations 200000 --lambda 10000 --training_file "
def generate_feature(train_file, test_file, object_file, train_feature_file,
test_feature_file):
logging.info('loading input data')
trn = pd.read_csv(train_file)
tst = pd.read_csv(test_file)
obj = pd.read_csv(object_file, header=None)
obj.columns = ['course_id', 'object', 'category', 'children', 'start']
n_trn = trn.shape[0]
trn.time = pd.to_datetime(trn.time)
tst.time = pd.to_datetime(tst.time)
df = pd.concat([trn, tst], axis=0)
# get last dates of courses
last_date = df[['course_id', 'time']].groupby('course_id',
as_index=False).max()
last_date.columns = ['course_id', 'last_date']
# extract object information
obj.children.fillna('', inplace=True)
obj['n_children'] = obj.children.apply(
lambda x: int(np.log2(1 + len(x.split()))))
obj.start.replace('null', '1999-01-01 00:00:00', inplace=True)
obj.start = pd.to_datetime(obj.start)
obj = pd.merge(obj, last_date, on='course_id', how='left')
obj['obj_days_before_last_date'] = (
obj.last_date - obj.start).apply(lambda x: pd.Timedelta(x).days)
obj.ix[obj.obj_days_before_last_date > 30,
'obj_days_before_last_date'] = 30
# merge log data with last coursedate and object information
df = pd.merge(df, last_date, on='course_id', how='left')
df = pd.merge(
df,
obj[['object', 'category', 'n_children', 'obj_days_before_last_date']],
on='object',
how='left')
df['days_before_last_date'] = (
df.last_date - df.time).apply(lambda x: pd.Timedelta(x).days)
df['weeks_before_last_date'] = df.days_before_last_date // 7
df.ix[df.weeks_before_last_date == 4, 'weeks_before_last_date'] = 3
df['last_month'] = df.last_date.apply(lambda x: x.month)
df['obj_10_days_after_last_date'] = df.obj_days_before_last_date.apply(
lambda x: 1 if x < 0 and x >= -10 else 0)
df.obj_days_before_last_date = df.obj_days_before_last_date.apply(
lambda x: np.sign(x) * int(np.log2(1 + np.sign(x) * x)) \
if ~pd.isnull(x) else x
)
df.drop(['time', 'last_date'], axis=1, inplace=True)
df.set_index('enrollment_id', inplace=True)
X = encode_categorical_features(df, n=n_trn, min_obs=100, nan_as_var=True)
X = X.tocsr()
dump_svmlight_file(X[:n_trn],
trn.enrollment_id.values,
train_feature_file,
zero_based=False)
dump_svmlight_file(X[n_trn:],
tst.enrollment_id.values,
test_feature_file,
zero_based=False)
def dump_data(x, y, file_output):
datasets.dump_svmlight_file(x, y, file_output)
os.remove("%s_tmp" % file_output)
def test(self):
X_train, X_test, y_train, y_test = train_test_split(*load_breast_cancer(return_X_y=True),
test_size=0.1, random_state=2)
train_data = lgb.Dataset(X_train, label=y_train)
valid_data = train_data.create_valid(X_test, label=y_test)
params = {
"objective": "binary",
"metric": "auc",
"min_data": 10,
"num_leaves": 15,
"verbose": -1,
"num_threads": 1,
"max_bin": 255,
"gpu_use_dp": True
}
bst = lgb.Booster(params, train_data)
bst.add_valid(valid_data, "valid_1")
for i in range(20):
bst.update()
if i % 10 == 0:
print(bst.eval_train(), bst.eval_valid())
self.assertEqual(bst.current_iteration(), 20)
self.assertEqual(bst.num_trees(), 20)
self.assertEqual(bst.num_model_per_iteration(), 1)
self.assertAlmostEqual(bst.lower_bound(), -2.9040190126976606)
self.assertAlmostEqual(bst.upper_bound(), 3.3182142872462883)
bst.save_model("model.txt")
pred_from_matr = bst.predict(X_test)
with tempfile.NamedTemporaryFile() as f:
tname = f.name
with open(tname, "w+b") as f:
dump_svmlight_file(X_test, y_test, f)
pred_from_file = bst.predict(tname)
os.remove(tname)
np.testing.assert_allclose(pred_from_matr, pred_from_file)
# check saved model persistence
bst = lgb.Booster(params, model_file="model.txt")
os.remove("model.txt")
pred_from_model_file = bst.predict(X_test)
# we need to check the consistency of model file here, so test for exact equal
np.testing.assert_array_equal(pred_from_matr, pred_from_model_file)
# check early stopping is working. Make it stop very early, so the scores should be very close to zero
pred_parameter = {"pred_early_stop": True, "pred_early_stop_freq": 5, "pred_early_stop_margin": 1.5}
pred_early_stopping = bst.predict(X_test, **pred_parameter)
# scores likely to be different, but prediction should still be the same
np.testing.assert_array_equal(np.sign(pred_from_matr), np.sign(pred_early_stopping))
# test that shape is checked during prediction
bad_X_test = X_test[:, 1:]
bad_shape_error_msg = "The number of features in data*"
np.testing.assert_raises_regex(lgb.basic.LightGBMError, bad_shape_error_msg,
bst.predict, bad_X_test)
np.testing.assert_raises_regex(lgb.basic.LightGBMError, bad_shape_error_msg,
bst.predict, sparse.csr_matrix(bad_X_test))
np.testing.assert_raises_regex(lgb.basic.LightGBMError, bad_shape_error_msg,
bst.predict, sparse.csc_matrix(bad_X_test))
with open(tname, "w+b") as f:
dump_svmlight_file(bad_X_test, y_test, f)
np.testing.assert_raises_regex(lgb.basic.LightGBMError, bad_shape_error_msg,
bst.predict, tname)
with open(tname, "w+b") as f:
dump_svmlight_file(X_test, y_test, f, zero_based=False)
np.testing.assert_raises_regex(lgb.basic.LightGBMError, bad_shape_error_msg,
bst.predict, tname)
os.remove(tname)
# test_len,
]).tocsr()
print X.shape
print X_t.shape
skf = KFold(n_splits=5, shuffle=True, random_state=seed).split(X)
for ind_tr, ind_te in skf:
X_train = X[ind_tr]
X_test = X[ind_te]
y_train = y[ind_tr]
y_test = y[ind_te]
break
dump_svmlight_file(X, y, inDir + "/input/X_tfidf.svm")
del X
dump_svmlight_file(X_t, np.zeros(X_t.shape[0]), inDir + "/input/X_t_tfidf.svm")
del X_t
def oversample(X_ot, y, p=0.165):
pos_ot = X_ot[y == 1]
neg_ot = X_ot[y == 0]
#p = 0.165
scale = ((pos_ot.shape[0] * 1.0 /
(pos_ot.shape[0] + neg_ot.shape[0])) / p) - 1
while scale > 1:
neg_ot = ssp.vstack([neg_ot, neg_ot]).tocsr()
scale -= 1
neg_ot = ssp.vstack([neg_ot,
imax = COL_LIMIT if COL_LIMIT < Y.shape[1] else Y.shape[1]
for i in range(imax):
i = 53
y = Y[:,i]
took_indexes = (y != -1).nonzero()[0]
print "Got indexes"
if len(took_indexes) < 1000: continue
y = y[took_indexes]
x = X[took_indexes]
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.5, random_state=0)
print "Dumping"
datasets.dump_svmlight_file(x_train, y_train, "tmp/train%d" % i)
datasets.dump_svmlight_file(x_test, y_test, "tmp/test%d" % i)
os.system("cd tmp && csplit -s test%d 3 && mv xx01 test%d && rm xx00" % (i, i))
os.system("cd tmp && csplit -s train%d 3 && mv xx01 train%d && rm xx00" % (i, i))
# os.system("svm-train -g 2 -c 8 -q tmp/train%d tmp/model%d" % (i, i))
os.system("svm-train -g 0.03125 -c 32 tmp/train%d tmp/model%d" % (i, i))
os.system("svm-predict tmp/test%d tmp/model%d tmp/predicted%d" % (i, i, i))
y_predicted = np.array(map((lambda n: np.float64(n)), open("tmp/predicted%d" % i).read().split("\n")[0:-1]))
deltas = np.subtract(y_predicted, y_test)
rms = np.sqrt(np.mean(deltas**2))
print "Subject %s: RMS %f" % (y_labels[i], rms)
# os.system("rm tmp/test%d tmp/train%d tmp/predicted%d" % (i, i, i))
DELTAS.append(deltas)
#pred_train = np.hstack(( pred_train, np.reshape(pred_label_train6,(-1,1)) ))
#pred_test = np.hstack(( pred_test, np.reshape(pred_label_test6,(-1,1)) ))
#vw
print "Vowpal Wabbit"
ss = StandardScaler()
train1_data_norm = ss.fit_transform(train1_data)
train2_data_norm = ss.transform(train2_data)
test_data_norm = ss.transform(test_data)
ntrain1_label = train_label1.copy()
ntrain1_label.values[np.where(ntrain1_label == 0)] = -1
ntrain2_label = train_label2.copy()
ntrain2_label.values[np.where(ntrain2_label == 0)] = -1
dump_svmlight_file(train1_data,
ntrain1_label,
"train1.vw",
zero_based=False)
dump_svmlight_file(train2_data,
ntrain2_label,
"train2.vw",
zero_based=False)
dump_svmlight_file(test_data,
np.zeros((test_data_norm.shape[0], )),
"test.vw",
zero_based=False)
print 1
of = open("vw_train1set.csv", "w")
of2 = open("vw_train2set.csv", "w")
of3 = open("vw_testset.csv", "w")
fi = open("train1.vw", "r")
logger.info("complete train %s feature extraction" %
column_list[column_list.index(key)])
print(column_list[column_list.index(key)] + " " +
"trainset vocab shape: " + str(features_train.shape))
#data format
logger.info("start data format %s" %
column_list[column_list.index(key)])
train_features_save_path = "./classifier_3_train_features_svm_format_files/" + column_list[
column_list.index(key)]
if not os.path.exists(train_features_save_path):
os.makedirs(train_features_save_path)
dump_svmlight_file(features_train,
label_train,
train_features_save_path + '/' +
column_list[column_list.index(key)] + '.txt',
zero_based=True,
comment=None,
query_id=None)
logger.info("complete data format %s" %
column_list[column_list.index(key)])
logger.info("complete all data format")
logger.info("start train model")
for column in column_list:
model_save_path = "./model_files_classifier_3/" + column
if not os.path.exists(model_save_path):
os.makedirs(model_save_path)
subprocess.call(
"./thundersvm-master/build/bin/thundersvm-train -c 100 -g 0.5 " +
"./classifier_3_train_features_svm_format_files/" + column + "/" +
def dump_svmlight_file(self, file):
data = np.array(self.data)
X = data[:, 0:2]
y = data[:, 2]
dump_svmlight_file(X, y, file)
from sklearn.datasets import dump_svmlight_file
from sklearn.externals import joblib
from sklearn.metrics import precision_score
iris = datasets.load_iris()
X = iris.data
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# use DMatrix for xgbosot
dtrain = xgb.DMatrix(X_train, label=y_train)
dtest = xgb.DMatrix(X_test, label=y_test)
# use svmlight file for xgboost
dump_svmlight_file(X_train, y_train, 'dtrain.svm', zero_based=True)
dump_svmlight_file(X_test, y_test, 'dtest.svm', zero_based=True)
dtrain_svm = xgb.DMatrix('dtrain.svm')
dtest_svm = xgb.DMatrix('dtest.svm')
# set xgboost params
param = {
'max_depth': 3, # the maximum depth of each tree
'eta': 0.3, # the training step for each iteration
'silent': 1, # logging mode - quiet
'objective': 'multi:softprob', # error evaluation for multiclass training
'num_class': 3} # the number of classes that exist in this datset
num_round = 20 # the number of training iterations
#------------- numpy array ------------------
# training and testing - numpy matrices
datasets = ['webspam_u']
for dataset in datasets:
if os.path.isfile('/home/neyo/PycharmProjects/AUC/datasets/%s' %
(dataset)):
print('Loading dataset = %s......' % (dataset), end=' ')
X, y = load_svmlight_file(
'/home/neyo/PycharmProjects/AUC/datasets/%s' % (dataset))
print('Done! Converting to binary......', end=' ')
m = np.mean(y)
INDEX = np.argwhere(y > m)
index = np.argwhere(y <= m)
y[INDEX] = 1
y[index] = -1
print('Done! Normalizing......', end=' ')
X = preprocessing.normalize(X)
print('Done! Dumping into file......', end=' ')
dump_svmlight_file(
X,
y,
'/home/neyo/PycharmProjects/AUC/bi-datasets/%s' % (dataset),
zero_based=False)
print('Done!')
else:
pass
n_classes = 2
split_params = {'test_size': 0.2, 'random_state': seed}
X, y = datasets.make_classification(n_samples=n_samples,
class_sep=0.4,
n_features=n_features,
n_classes=n_classes,
random_state=seed)
x_train, x_test, y_train, y_test = model_selection.train_test_split(
X, y, **split_params)
# Save data in .svm format
train_path = os.path.abspath('x_train.svm')
test_path = os.path.abspath('x_test.svm')
datasets.dump_svmlight_file(x_train, y_train, train_path)
datasets.dump_svmlight_file(x_test, y_test, test_path)
clf = XGBClassifier(port=8085, nclasses=n_classes)
booster_params = {
"max_depth": 10,
"subsample": 0.8,
"eta": 0.3,
"drop_rate": 0.4,
"skip_drop": 0.4
}
clf.fit([train_path, test_path],
booster="dart",
iterations=20,
**booster_params)
x_test = [cv.preprocessor(x) for x in x_test]
x_train_pre = x_train
x_test_pre = x_test
y_train = [float(y)
for y in y_train] # float para permitir utilizar no classificador
y_test = [float(y) for y in y_test]
union = Pipeline([(
'features',
FeatureUnion(transformer_list=[(
'tfdif_features',
Pipeline([('word',
TfidfVectorizer(ngram_range=(1,
2))) #,
]))]))])
x_train = union.fit_transform(x_train)
print('fold ' + str(index) + ', x_train.shape: ', x_train.shape)
dump_svmlight_file(
x_train, y_train,
"dataset/representations/" + name_dataset + '/train' + str(index))
x_test = union.transform(x_test)
print('fold ' + str(index) + ', x_test.shape: ', x_test.shape)
dump_svmlight_file(
x_test, y_test,
"dataset/representations/" + name_dataset + '/test' + str(index))
print("Time End: %f" % (timeit.default_timer() - ini))
df,
pd.DataFrame(array, columns=map(lambda x: 'ATTRS' + str(x), range(100)))
],
axis=1)
df.drop("ATTRS_STR", axis=1, inplace=True)
del array
# ---------- 将类别转换为one-hot ----------
df = pd.concat([df, pd.get_dummies(df['CATID'], prefix='CATID')], axis=1)
df.drop('CATID', axis=1, inplace=True)
# ---------- 扔掉前三列ID ----------
df.drop(['ID', 'ADVID', 'GOODSID'], axis=1, inplace=True)
print df.head()
# 写出文件
#with open('goods_vectors_newnew.csv', 'a') as f:
# for row in df.values:
# f.write(",".join(map(str, row)) + '\n')
#sqldf = sqlContext.createDataFrame(df)
#sqldf.save(path='/user/mjoys/goods_vectors_new', mode='overwrite')
print df.info()
print("Writing...")
# 将数据转化成libsvm
from sklearn.datasets import dump_svmlight_file
dump_svmlight_file(df.values, df.index.values, 'goods_vectors.libsvm')
print("Done")
def tovw(x, y=None, sample_weight=None, convert_labels=False):
"""Convert array or sparse matrix to Vowpal Wabbit format
Parameters
----------
x : {array-like, sparse matrix}, shape (n_samples, n_features)
Training vector, where n_samples is the number of samples and
n_features is the number of features.
y : {array-like}, shape (n_samples,), optional
Target vector relative to X.
sample_weight : {array-like}, shape (n_samples,), optional
sample weight vector relative to X.
convert_labels : {bool} convert labels of the form [0,1] to [-1,1]
Returns
-------
out : {array-like}, shape (n_samples, 1)
Training vectors in VW string format
Examples
--------
>>> import pandas as pd
>>> from sklearn.feature_extraction.text import HashingVectorizer
>>> from vowpalwabbit.sklearn_vw import tovw
>>> X = pd.Series(['cat', 'dog', 'cat', 'cat'], name='catdog')
>>> y = pd.Series([-1, 1, -1, -1], name='label')
>>> hv = HashingVectorizer()
>>> hashed = hv.fit_transform(X)
>>> tovw(x=hashed, y=y)
"""
use_truth = y is not None
use_weight = sample_weight is not None
if use_truth:
x, y = check_X_y(x, y, accept_sparse=True)
else:
x = check_array(x, accept_sparse=True)
if use_weight:
sample_weight = check_array(sample_weight,
accept_sparse=False,
ensure_2d=False,
dtype=np.int,
order="C")
if sample_weight.ndim != 1:
raise ValueError("Sample weights must be 1D array or scalar")
if sample_weight.shape != (x.shape[0], ):
raise ValueError("Sample weight shape == {}, expected {}".format(
sample_weight.shape, (x.shape[0], )))
else:
sample_weight = np.ones(x.shape[0], dtype=np.int)
# convert labels of the form [0,1] to [-1,1]
if convert_labels:
y = np.where(y < 1, -1, 1)
rows, cols = x.shape
# check for invalid characters if array has string values
if x.dtype.char == 'S':
for row in rows:
for col in cols:
x[row, col] = INVALID_CHARS.sub('.', x[row, col])
# convert input to svmlight format
s = io.BytesIO()
dump_svmlight_file(x, np.zeros(rows), s)
# parse entries to construct VW format
rows = s.getvalue().decode('ascii').split('\n')[:-1]
out = []
for idx, row in enumerate(rows):
truth = y[idx] if use_truth else 1
weight = sample_weight[idx]
features = row.split('0 ', 1)[1]
# only using a single namespace and no tags
out.append(('{y} {w} |{ns} {x}'.format(y=truth,
w=weight,
ns=DEFAULT_NS,
x=features)))
s.close()
return out
import argparse
from sklearn.datasets import load_svmlight_file, dump_svmlight_file
import scipy.sparse as sp
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Concatenates datasets by features.')
parser.add_argument('datasets', metavar='data', type=str, nargs='+',
help='datasets which will be concatenate')
parser.add_argument('-o', '--output', type=str,
help='file to sava the concatenated datasets')
args = parser.parse_args()
X_out = None
for data in args.datasets:
X, y = load_svmlight_file(data)
X_out = X if X_out is None else sp.hstack((X_out, X))
if not args.output is None:
dump_svmlight_file(X_out, y, args.output)
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.datasets import dump_svmlight_file
import numpy as np
import os
y = [0,0,0,0]
i = 0
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(pre_svm)
transformer = TfidfTransformer()
tfidf = transformer.fit_transform(X)
f = open('/Users/arrowlittle/Desktop/data/wiki_libsvm.txt', 'w')
dump_svmlight_file(tfidf, y, f, zero_based=False)
f.close()
print tfidf.toarray()
#split data
from pyspark.ml.classification import NaiveBayes
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
data = spark.read.format("libsvm") \
.load("/Users/arrowlittle/Desktop/data/wiki_libsvm.txt")
splits = data.randomSplit([0.9, 0.1], 1234)
train = splits[0]
]).tocsr()
print X.shape
print X_t.shape
skf = KFold(n_splits=5, shuffle=True, random_state=SEED).split(X)
for ind_tr, ind_te in skf:
X_train = X[ind_tr]
X_test = X[ind_te]
y_train = y[ind_tr]
y_test = y[ind_te]
break
dump_svmlight_file(X,y,PATH+"X_tfidf.svm")
del X
dump_svmlight_file(X_t,np.zeros(X_t.shape[0]),PATH+"X_t_tfidf.svm")
del X_t
def oversample(X_ot,y,p=0.175):
pos_ot = X_ot[y==1]
neg_ot = X_ot[y==0]
#p = 0.165
scale = ((pos_ot.shape[0]*1.0 / (pos_ot.shape[0] + neg_ot.shape[0])) / p) - 1
while scale > 1:
neg_ot = ssp.vstack([neg_ot, neg_ot]).tocsr()
scale -=1
neg_ot = ssp.vstack([neg_ot, neg_ot[:int(scale * neg_ot.shape[0])]]).tocsr()
ot = ssp.vstack([pos_ot, neg_ot]).tocsr()
y=np.zeros(ot.shape[0])
def run(self,
x_train,
y_train,
x_test,
y_test,
x_validation_set=None,
y_validation_set=None,
meta=None):
"""Run factorization machine model against train and test data
Parameters
----------
x_train : {array-like, matrix}, shape = [n_train, n_features]
Training data
y_train : numpy array of shape [n_train]
Target values
x_test: {array-like, matrix}, shape = [n_test, n_features]
Testing data
y_test : numpy array of shape [n_test]
Testing target values
x_validation_set: optional, {array-like, matrix}, shape = [n_train, n_features]
Validation data (only for SGDA)
y_validation_set: optional, numpy array of shape [n_train]
Validation target data (only for SGDA)
meta: optional, numpy array of shape [n_features]
Grouping input variables
Return
-------
Returns `namedtuple` with the following properties:
predictions: array [n_samples of x_test]
Predicted target values per element in x_test.
global_bias: float
If k0 is True, returns the model's global bias w0
weights: array [n_features]
If k1 is True, returns the model's weights for each features Wj
pairwise_interactions: numpy matrix [n_features x k2]
Matrix with pairwise interactions Vj,f
rlog: pandas dataframe [nrow = num_iter]
`pandas` DataFrame with measurements about each iteration
"""
from sklearn.datasets import dump_svmlight_file
TMP_SUFFIX = '.pywfm'
train_fd = tempfile.NamedTemporaryFile(suffix=TMP_SUFFIX,
dir=self.__temp_path)
test_fd = tempfile.NamedTemporaryFile(suffix=TMP_SUFFIX,
dir=self.__temp_path)
out_fd = tempfile.NamedTemporaryFile(suffix=TMP_SUFFIX,
dir=self.__temp_path)
model_fd = tempfile.NamedTemporaryFile(suffix=TMP_SUFFIX,
dir=self.__temp_path)
# converts train and test data to libSVM format
dump_svmlight_file(x_train, y_train, train_fd)
train_fd.seek(0)
dump_svmlight_file(x_test, y_test, test_fd)
test_fd.seek(0)
# builds arguments array
args = [
os.path.join(self.__libfm_path, "libFM"), '-task',
"%s" % self.__task, '-train',
"%s" % train_fd.name, '-test',
"%s" % test_fd.name, '-dim',
"'%s'" % self.__dim, '-init_stdev',
"%g" % self.__init_stdev, '-iter',
"%d" % self.__num_iter, '-method',
"%s" % self.__learning_method, '-out',
"%s" % out_fd.name, '-verbosity',
"%d" % self.__verbose, '-save_model',
"%s" % model_fd.name
]
# appends rlog if true
rlog_fd = None
if self.__rlog:
rlog_fd = tempfile.NamedTemporaryFile(suffix=TMP_SUFFIX,
dir=self.__temp_path)
args.extend(['-rlog', "%s" % rlog_fd.name])
# appends seed if given
if self.__seed:
args.extend(['-seed', "%d" % self.__seed])
# appends arguments that only work for certain learning methods
if self.__learning_method in ['sgd', 'sgda']:
args.extend(['-learn_rate', "%.5f" % self.__learn_rate])
if self.__learning_method in ['sgd', 'sgda', 'als']:
args.extend(['-regular', "'%s'" % self.__regularization])
# adds validation if sgda
# if validation_set is none, libFM will throw error hence, I'm not doing any validation
validation_fd = None
if self.__learning_method == 'sgda' and (
x_validation_set is not None and y_validation_set is not None):
validation_fd = tempfile.NamedTemporaryFile(suffix=TMP_SUFFIX,
dir=self.__temp_path)
dump_svmlight_file(x_validation_set, y_validation_set,
validation_fd.name)
args.extend(['-validation', "%s" % validation_fd.name])
# if meta data is given
meta_fd = None
if meta is not None:
meta_fd = tempfile.NamedTemporaryFile(suffix=TMP_SUFFIX,
dir=self.__temp_path,
text=True)
# write group ids
for group_id in meta:
meta_fd.write("%s\n" % group_id)
args.extend(['-meta', "%s" % meta_fd.name])
meta_fd.seek(0)
# if silent redirects all output
stdout = None
if self.__silent:
stdout = open(os.devnull, 'wb')
# call libfm with parsed arguments
# had unkown bug with "-dim" option on array. At the time was forced to
# concatenate string `args = ' '.join(args)` but looks like its working
# needs further tests
subprocess.call(args, shell=False, stdout=stdout)
# reads output file
preds = [float(p) for p in out_fd.read().split('\n') if p]
# "hidden" feature that allows users to save the model
# We use this to get the feature weights
# https://github.com/srendle/libfm/commit/19db0d1e36490290dadb530a56a5ae314b68da5d
import numpy as np
global_bias = None
weights = []
pairwise_interactions = []
# if 0 its global bias; if 1, weights; if 2, pairwise interactions
out_iter = 0
for line in model_fd.read().splitlines():
# checks which line is starting with #
if line.startswith('#'):
if "#global bias W0" in line:
out_iter = 0
elif "#unary interactions Wj" in line:
out_iter = 1
elif "#pairwise interactions Vj,f" in line:
out_iter = 2
else:
# check context get in previous step and adds accordingly
if out_iter == 0:
global_bias = float(line)
elif out_iter == 1:
weights.append(float(line))
elif out_iter == 2:
try:
pairwise_interactions.append(
[float(x) for x in line.split(' ')])
except ValueError as e:
pairwise_interactions.append(
0.0) #Case: no pairwise interactions used
pairwise_interactions = np.matrix(pairwise_interactions)
# parses rlog into dataframe
if self.__rlog:
# parses rlog into
import pandas as pd
rlog_fd.seek(0)
print os.stat(rlog_fd.name).st_size
rlog = pd.read_csv(rlog_fd.name, sep='\t')
rlog_fd.close()
else:
rlog = None
if self.__learning_method == 'sgda' and (
x_validation_set is not None and y_validation_set is not None):
validation_fd.close()
if meta is not None:
meta_fd.close()
# removes temporary output file after using
train_fd.close()
test_fd.close()
model_fd.close()
out_fd.close()
# return as named collection for multiple output
import collections
fm = collections.namedtuple('model', [
'predictions', 'global_bias', 'weights', 'pairwise_interactions',
'rlog'
])
return fm(preds, global_bias, weights, pairwise_interactions, rlog)
def test_feature_importances(self):
data = np.random.randn(100, 5)
target = np.array([0, 1] * 50)
dump_svmlight_file(data, target, temp_name)
xgb.encrypt_file(temp_name, temp_enc_name, sym_key_file)
features = ['Feature1', 'Feature2', 'Feature3', 'Feature4', 'Feature5']
dm = xgb.DMatrix({username: temp_enc_name}, feature_names=features)
params = {'objective': 'multi:softprob',
'eval_metric': 'mlogloss',
'eta': 0.3,
'num_class': 3}
bst = xgb.train(params, dm, num_boost_round=10)
# number of feature importances should == number of features
scores1 = bst.get_score()
scores2 = bst.get_score(importance_type='weight')
scores3 = bst.get_score(importance_type='cover')
scores4 = bst.get_score(importance_type='gain')
scores5 = bst.get_score(importance_type='total_cover')
scores6 = bst.get_score(importance_type='total_gain')
assert len(scores1) == len(features)
assert len(scores2) == len(features)
assert len(scores3) == len(features)
assert len(scores4) == len(features)
assert len(scores5) == len(features)
assert len(scores6) == len(features)
# check backwards compatibility of get_fscore
fscores = bst.get_fscore()
assert scores1 == fscores
dtrain = xgb.DMatrix({username: dpath + 'agaricus.txt.train.enc'})
dtest = xgb.DMatrix({username: dpath + 'agaricus.txt.test.enc'})
def fn(max_depth, num_rounds):
# train
params = {'max_depth': max_depth, 'eta': 1, 'verbosity': 0}
bst = xgb.train(params, dtrain, num_boost_round=num_rounds)
# predict
preds = bst.predict(dtest)[0]
contribs = bst.predict(dtest, pred_contribs=True)[0]
# result should be (number of features + BIAS) * number of rows
assert contribs.shape == (dtest.num_row(), dtest.num_col() + 1)
# sum of contributions should be same as predictions
np.testing.assert_array_almost_equal(np.sum(contribs, axis=1), preds)
# for max_depth, num_rounds in itertools.product(range(0, 3), range(1, 5)):
# yield fn, max_depth, num_rounds
# check that we get the right SHAP values for a basic AND example
# (https://arxiv.org/abs/1706.06060)
X = np.zeros((4, 2))
X[0, :] = 1
X[1, 0] = 1
X[2, 1] = 1
y = np.zeros(4)
y[0] = 1
param = {"max_depth": 2, "base_score": 0.0, "eta": 1.0, "lambda": 0}
dump_svmlight_file(X, y, temp_name)
xgb.encrypt_file(temp_name, temp_enc_name, sym_key_file)
bst = xgb.train(param, xgb.DMatrix({username: temp_enc_name}), 1)
dump_svmlight_file(X[0:1, :], np.zeros(1), temp_name)
xgb.encrypt_file(temp_name, temp_enc_name, sym_key_file)
out = bst.predict(xgb.DMatrix({username: temp_enc_name}), pred_contribs=True)[0]
#TODO(rishabh): enable pred_contribs
"""
assert out[0, 0] == 0.375
assert out[0, 1] == 0.375
assert out[0, 2] == 0.25
"""
def parse_model(model):
trees = []
r_exp = r"([0-9]+):\[f([0-9]+)<([0-9\.e-]+)\] yes=([0-9]+),no=([0-9]+).*cover=([0-9e\.]+)"
r_exp_leaf = r"([0-9]+):leaf=([0-9\.e-]+),cover=([0-9e\.]+)"
for tree in model.get_dump(with_stats=True):
lines = list(tree.splitlines())
trees.append([None for i in range(len(lines))])
for line in lines:
match = re.search(r_exp, line)
if match is not None:
ind = int(match.group(1))
while ind >= len(trees[-1]):
trees[-1].append(None)
trees[-1][ind] = {
"yes_ind": int(match.group(4)),
"no_ind": int(match.group(5)),
"value": None,
"threshold": float(match.group(3)),
"feature_index": int(match.group(2)),
"cover": float(match.group(6))
}
else:
match = re.search(r_exp_leaf, line)
ind = int(match.group(1))
while ind >= len(trees[-1]):
trees[-1].append(None)
trees[-1][ind] = {
"value": float(match.group(2)),
"cover": float(match.group(3))
}
return trees
def exp_value_rec(tree, z, x, i=0):
if tree[i]["value"] is not None:
return tree[i]["value"]
else:
ind = tree[i]["feature_index"]
if z[ind] == 1:
if x[ind] < tree[i]["threshold"]:
return exp_value_rec(tree, z, x, tree[i]["yes_ind"])
else:
return exp_value_rec(tree, z, x, tree[i]["no_ind"])
else:
r_yes = tree[tree[i]["yes_ind"]]["cover"] / tree[i]["cover"]
out = exp_value_rec(tree, z, x, tree[i]["yes_ind"])
val = out * r_yes
r_no = tree[tree[i]["no_ind"]]["cover"] / tree[i]["cover"]
out = exp_value_rec(tree, z, x, tree[i]["no_ind"])
val += out * r_no
return val
def exp_value(trees, z, x):
return np.sum([exp_value_rec(tree, z, x) for tree in trees])
def all_subsets(ss):
return itertools.chain(*map(lambda x: itertools.combinations(ss, x), range(0, len(ss) + 1)))
def shap_value(trees, x, i, cond=None, cond_value=None):
M = len(x)
z = np.zeros(M)
other_inds = list(set(range(M)) - set([i]))
if cond is not None:
other_inds = list(set(other_inds) - set([cond]))
z[cond] = cond_value
M -= 1
total = 0.0
for subset in all_subsets(other_inds):
if len(subset) > 0:
z[list(subset)] = 1
v1 = exp_value(trees, z, x)
z[i] = 1
v2 = exp_value(trees, z, x)
total += (v2 - v1) / (scipy.special.binom(M - 1, len(subset)) * M)
z[i] = 0
z[list(subset)] = 0
return total
def shap_values(trees, x):
vals = [shap_value(trees, x, i) for i in range(len(x))]
vals.append(exp_value(trees, np.zeros(len(x)), x))
return np.array(vals)
def interaction_values(trees, x):
M = len(x)
out = np.zeros((M + 1, M + 1))
for i in range(len(x)):
for j in range(len(x)):
if i != j:
out[i, j] = interaction_value(trees, x, i, j) / 2
svals = shap_values(trees, x)
main_effects = svals - out.sum(1)
out[np.diag_indices_from(out)] = main_effects
return out
def interaction_value(trees, x, i, j):
M = len(x)
z = np.zeros(M)
other_inds = list(set(range(M)) - set([i, j]))
total = 0.0
for subset in all_subsets(other_inds):
if len(subset) > 0:
z[list(subset)] = 1
v00 = exp_value(trees, z, x)
z[i] = 1
v10 = exp_value(trees, z, x)
z[j] = 1
v11 = exp_value(trees, z, x)
z[i] = 0
v01 = exp_value(trees, z, x)
z[j] = 0
total += (v11 - v01 - v10 + v00) / (scipy.special.binom(M - 2, len(subset)) * (M - 1))
z[list(subset)] = 0
return total
# test a simple and function
M = 2
N = 4
X = np.zeros((N, M))
X[0, :] = 1
X[1, 0] = 1
X[2, 1] = 1
y = np.zeros(N)
y[0] = 1
param = {"max_depth": 2, "base_score": 0.0, "eta": 1.0, "lambda": 0}
#TODO(rishabh): enable pred_contribs
"""
def combine_feat(feat_names, feat_path_name):
print("==================================================")
print("Combine features...")
######################
## Cross-validation ##
######################
print("For cross-validation...")
## for each run and fold
for run in range(1, config.n_runs + 1):
## use 33% for training and 67 % for validation
## so we switch trainInd and validInd
for fold in range(1, config.n_folds + 1):
print("Run: %d, Fold: %d" % (run, fold))
path = "%s/Run%d/Fold%d" % (config.feat_folder, run, fold)
save_path = "%s/%s/Run%d/Fold%d" % (config.feat_folder,
feat_path_name, run, fold)
if not os.path.exists(save_path):
os.makedirs(save_path)
for i, (feat_name, transformer) in enumerate(feat_names):
## load train feat
feat_train_file = "%s/train.%s.feat.pkl" % (path, feat_name)
with open(feat_train_file, "rb") as f:
x_train = cPickle.load(f)
if len(x_train.shape) == 1:
x_train.shape = (x_train.shape[0], 1)
## load valid feat
feat_valid_file = "%s/valid.%s.feat.pkl" % (path, feat_name)
with open(feat_valid_file, "rb") as f:
x_valid = cPickle.load(f)
if len(x_valid.shape) == 1:
x_valid.shape = (x_valid.shape[0], 1)
## align feat dim
dim_diff = abs(x_train.shape[1] - x_valid.shape[1])
if x_valid.shape[1] < x_train.shape[1]:
x_valid = hstack(
[x_valid,
np.zeros((x_valid.shape[0], dim_diff))]).tocsr()
elif x_valid.shape[1] > x_train.shape[1]:
x_train = hstack(
[x_train,
np.zeros((x_train.shape[0], dim_diff))]).tocsr()
## apply transformation
x_train = transformer.fit_transform(x_train)
x_valid = transformer.transform(x_valid)
## stack feat
if i == 0:
X_train, X_valid = x_train, x_valid
else:
try:
X_train, X_valid = hstack([X_train, x_train]), hstack(
[X_valid, x_valid])
except:
X_train, X_valid = np.hstack(
[X_train, x_train]), np.hstack([X_valid, x_valid])
print("Combine {:>2}/{:>2} feat: {} ({}D)".format(
i + 1, len(feat_names), feat_name, x_train.shape[1]))
print("Feat dim: {}D".format(X_train.shape[1]))
## load label
# train
info_train = pd.read_csv("%s/train.info" % (save_path))
## change it to zero-based for multi-classification in xgboost
Y_train = info_train["median_relevance"] - 1
# valid
info_valid = pd.read_csv("%s/valid.info" % (save_path))
Y_valid = info_valid["median_relevance"] - 1
## dump feat
dump_svmlight_file(X_train, Y_train, "%s/train.feat" % (save_path))
dump_svmlight_file(X_valid, Y_valid, "%s/valid.feat" % (save_path))
##########################
## Training and Testing ##
##########################
print("For training and testing...")
path = "%s/All" % (config.feat_folder)
save_path = "%s/%s/All" % (config.feat_folder, feat_path_name)
if not os.path.exists(save_path):
os.makedirs(save_path)
for i, (feat_name, transformer) in enumerate(feat_names):
## load train feat
feat_train_file = "%s/train.%s.feat.pkl" % (path, feat_name)
with open(feat_train_file, "rb") as f:
x_train = cPickle.load(f)
if len(x_train.shape) == 1:
x_train.shape = (x_train.shape[0], 1)
## load test feat
feat_test_file = "%s/test.%s.feat.pkl" % (path, feat_name)
with open(feat_test_file, "rb") as f:
x_test = cPickle.load(f)
if len(x_test.shape) == 1:
x_test.shape = (x_test.shape[0], 1)
## align feat dim
dim_diff = abs(x_train.shape[1] - x_test.shape[1])
if x_test.shape[1] < x_train.shape[1]:
x_test = hstack([x_test,
np.zeros((x_test.shape[0], dim_diff))]).tocsr()
elif x_test.shape[1] > x_train.shape[1]:
x_train = hstack([x_train,
np.zeros((x_train.shape[0], dim_diff))]).tocsr()
## apply transformation
x_train = transformer.fit_transform(x_train)
x_test = transformer.transform(x_test)
## stack feat
if i == 0:
X_train, X_test = x_train, x_test
else:
try:
X_train, X_test = hstack([X_train,
x_train]), hstack([X_test, x_test])
except:
X_train, X_test = np.hstack([X_train, x_train
]), np.hstack([X_test, x_test])
print("Combine {:>2}/{:>2} feat: {} ({}D)".format(
i + 1, len(feat_names), feat_name, x_train.shape[1]))
print("Feat dim: {}D".format(X_train.shape[1]))
## load label
# train
info_train = pd.read_csv("%s/train.info" % (save_path))
## change it to zero-based for multi-classification in xgboost
Y_train = info_train["median_relevance"] - 1
# test
info_test = pd.read_csv("%s/test.info" % (save_path))
Y_test = info_test["median_relevance"] - 1
## dump feat
dump_svmlight_file(X_train, Y_train, "%s/train.feat" % (save_path))
dump_svmlight_file(X_test, Y_test, "%s/test.feat" % (save_path))
def test_dump():
X_sparse, y_dense = load_svmlight_file(datafile)
X_dense = X_sparse.toarray()
y_sparse = sp.csr_matrix(y_dense)
# slicing a csr_matrix can unsort its .indices, so test that we sort
# those correctly
X_sliced = X_sparse[np.arange(X_sparse.shape[0])]
y_sliced = y_sparse[np.arange(y_sparse.shape[0])]
for X in (X_sparse, X_dense, X_sliced):
for y in (y_sparse, y_dense, y_sliced):
for zero_based in (True, False):
for dtype in [np.float32, np.float64, np.int32, np.int64]:
f = BytesIO()
# we need to pass a comment to get the version info in;
# LibSVM doesn't grok comments so they're not put in by
# default anymore.
if (sp.issparse(y) and y.shape[0] == 1):
# make sure y's shape is: (n_samples, n_labels)
# when it is sparse
y = y.T
# Note: with dtype=np.int32 we are performing unsafe casts,
# where X.astype(dtype) overflows. The result is
# then platform dependent and X_dense.astype(dtype) may be
# different from X_sparse.astype(dtype).asarray().
X_input = X.astype(dtype)
dump_svmlight_file(X_input, y, f, comment="test",
zero_based=zero_based)
f.seek(0)
comment = f.readline()
comment = str(comment, "utf-8")
assert "scikit-learn %s" % sklearn.__version__ in comment
comment = f.readline()
comment = str(comment, "utf-8")
assert ["one", "zero"][zero_based] + "-based" in comment
X2, y2 = load_svmlight_file(f, dtype=dtype,
zero_based=zero_based)
assert X2.dtype == dtype
assert_array_equal(X2.sorted_indices().indices, X2.indices)
X2_dense = X2.toarray()
if sp.issparse(X_input):
X_input_dense = X_input.toarray()
else:
X_input_dense = X_input
if dtype == np.float32:
# allow a rounding error at the last decimal place
assert_array_almost_equal(
X_input_dense, X2_dense, 4)
assert_array_almost_equal(
y_dense.astype(dtype, copy=False), y2, 4)
else:
# allow a rounding error at the last decimal place
assert_array_almost_equal(
X_input_dense, X2_dense, 15)
assert_array_almost_equal(
y_dense.astype(dtype, copy=False), y2, 15)
from sklearn.datasets import make_regression
from sklearn.datasets import dump_svmlight_file
import numpy as np
X, y = make_regression(
**{
'n_samples': 1000000,
'n_features': 50,
'n_informative': 4,
'n_targets': 1,
'random_state': 37
})
output = 'regression.binary'
# data = dump_svmlight_file(X, y, output)
data = dump_svmlight_file(X, y, output, zero_based=False)
print(data)
def create_svm_file(df, features_X, path):
"""
Convert a pandas DF into a lib-SVM format!
Args:
- df (pd DF) : Pandas DF - needs to have the columns, that are passed
in the features_X argument!
- features_X (list) : list of strings with the names of the attributes
in df we want to use as feature!
- path (string) : path (incl. DF_Name) to save the lib-SVM at!
[on top of "data/processed/Ranking/tf_ranking/"]
e.g. "CV/1.txt"
Return:
- save the LIB-SVM DF
- return the true responses of the passed df
(used to calc the F1-Score later on!)
"""
# [1] Check Input:
# - all feature names in df, so we can select the corresponding cols?
for _feat in features_X:
if _feat not in df.columns.values:
raise ValueError(_feat + "is not a columname in df")
# - features_X must start with "transport_mode"
# (needed to create valid predicitons)
if features_X[0] != "transport_mode":
raise ValueError("'features_X' must start with 'transport_mode'")
# - is the path, already defined?
# split folder and file name and check for the existence of the folder
folder = path.split("/")
folder = folder[:(len(folder) - 1)]
folder = "".join(folder)
if not os.path.isdir("data/processed/Ranking/tf_ranking/" + str(folder)):
raise ValueError(
str(folder) +
" is not existent in 'data/processed/Ranking/tf_ranking/'")
# [2] Clean the DF and get it ready!
# - Sort the SIDs
df.sort_values("sid", inplace=True)
# - drop rows, that have the same trans_mode multiple times for a single sid!
df = df.drop_duplicates(['sid', 'transport_mode'], keep='first')
# [3] Create ranking target
# - if click_mode we mark the target with "1" and the irrelevant ones as "0"
if 'click_mode' in df.columns:
print("Build LTR labels")
# T1 for target <--> 0 else
df = df.assign(target=df.apply(
lambda x: 1 if x.click_mode == x.transport_mode else 0, axis=1))
else:
# If test set every entry gets zeri for a label
print("Assign label 0 for test set")
df = df.assign(target=0)
# [4] Spit the DF into Target & Feature + extract the SIDs
# - we pass these to svm-converter to create a libsvm DF!
X = df[features_X]
y = df["target"]
query_id = df.sid
path = "data/processed/Ranking/tf_ranking/" + str(path)
# [5] Save the SVM_File on top of: "data/processed/Ranking/tf_ranking"
print("Dump file")
dump_svmlight_file(X=X, y=y, f=path, query_id=query_id, zero_based=False)
# [6] Return the Values of the true click_modes [needed for metrics!]
return np.array(df.drop_duplicates(["sid"]).click_mode)