def read_file():
file_content = pd.read_csv('train.csv')
exc_cols = [u'Id', u'Response']
cols = [c for c in file_content.columns if c not in exc_cols]
train_datas = file_content.ix[:, cols]
train_lables = file_content['Response'].values
test_file = pd.read_csv('test.csv')
test_ids = test_file['Id'].values
test_datas = test_file.ix[:, [c for c in test_file.columns if c not in [u'Id']]]
# 填充平均值
test_datas = test_datas.fillna(-1)
train_datas = train_datas.fillna(-1)
all_datas = pd.concat([train_datas, test_datas], axis=0)
# 对数据进行一下划分
categoricalVariables = ["Product_Info_1", "Product_Info_2", "Product_Info_3", "Product_Info_5", "Product_Info_6", "Product_Info_7", "Employment_Info_2", "Employment_Info_3", "Employment_Info_5", "InsuredInfo_1", "InsuredInfo_2", "InsuredInfo_3", "InsuredInfo_4", "InsuredInfo_5", "InsuredInfo_6", "InsuredInfo_7", "Insurance_History_1", "Insurance_History_2", "Insurance_History_3", "Insurance_History_4", "Insurance_History_7", "Insurance_History_8", "Insurance_History_9", "Family_Hist_1", "Medical_History_2", "Medical_History_3", "Medical_History_4", "Medical_History_5", "Medical_History_6", "Medical_History_7", "Medical_History_8", "Medical_History_9", "Medical_History_10", "Medical_History_11", "Medical_History_12", "Medical_History_13", "Medical_History_14", "Medical_History_16", "Medical_History_17", "Medical_History_18", "Medical_History_19", "Medical_History_20", "Medical_History_21", "Medical_History_22", "Medical_History_23", "Medical_History_25", "Medical_History_26", "Medical_History_27", "Medical_History_28", "Medical_History_29", "Medical_History_30", "Medical_History_31", "Medical_History_33", "Medical_History_34", "Medical_History_35", "Medical_History_36", "Medical_History_37", "Medical_History_38", "Medical_History_39", "Medical_History_40", "Medical_History_41"]
all_file_data = all_datas.ix[:, [c for c in all_datas.columns if c not in categoricalVariables]]
all_file_cate = all_datas.ix[:, [c for c in categoricalVariables]]
# 归一化 对数值数据
scalar_this = StandardScaler()
scalar_this.fit_transform(all_file_data)
# 重新组合数据
train_datas = pd.concat([all_file_data[:train_datas.shape[0]], all_file_cate[:train_datas.shape[0]]], axis=1)
test_datas = pd.concat([all_file_data[file_content.shape[0]:], all_file_cate[file_content.shape[0]:]], axis=1)
# 向量化
train_datas = DictVectorizer().fit_transform(train_datas.to_dict(outtype='records')).toarray()
test_datas = DictVectorizer().fit_transform(test_datas.to_dict(outtype='records')).toarray()
return (train_datas, train_lables, test_ids, test_datas)
def boston_DBSCAN(class_num=0):
'''给出Boston房价数据集中class_num类，数据形式为归一化数据列
:parameter
————
class_num:类号，读取函数所返回的类别号
:returns
————
x_boston:波士顿数据集中class_num类的自变量，归一化数据列，共13列
y_boston:波士顿数据集中class_num类自变量，归一化数据列，共1列
'''
# 读取全数据集
bostondata = load_boston()
boston_X = bostondata.data
boston_y = bostondata.target
boston_full = np.c_[boston_X, boston_y]
# 进行全数据集归一化
scale = StandardScaler()
boston_full = scale.fit_transform(boston_full)
# 数据集降维为3维，方便可视化调参。
pca = PCA(n_components=3)
boston_full3 = pca.fit_transform(boston_full)
# 分类
clt = DBSCAN(eps=0.8, min_samples=5, n_jobs=4)
label3 = clt.fit_predict(X=boston_full3)
# 给定输出数据
group0_boston = boston_full[label3 == 0]
x_boston = group0_boston[:, 0:-2]
y_boston = group0_boston[:, -1]
return x_boston, y_boston
def load_UCI_Credit_Card_data(infile=None, balanced=True, seed=5):
X = []
y = []
sids = []
with open(infile, "r") as fi:
fi.readline()
reader = csv.reader(fi)
for row in reader:
sids.append(row[0])
X.append(row[1:-1])
y0 = int(row[-1])
if y0 == 0:
y0 = -1
y.append(y0)
y = np.array(y)
if balanced:
X, y = balance_X_y(X, y, seed)
X = np.array(X, dtype=np.float32)
y = np.array(y, dtype=np.float32)
encoder = OneHotEncoder(categorical_features=[1, 2, 3])
encoder.fit(X)
X = encoder.transform(X).toarray()
X, y = shuffle_X_y(X, y, seed)
scale_model = StandardScaler()
X = scale_model.fit_transform(X)
return X, np.expand_dims(y, axis=1)
def get_data(args, logger, debug):
'''Get data.'''
# Get data:
train_data, val_data, test_data = _get_data(args, logger)
debug(f'train size = {len(train_data):,} | val size = {len(val_data):,} |'
f' test size = {len(test_data):,}')
if args.dataset_type == 'classification':
class_sizes = get_class_sizes(args.data_df)
debug('Class sizes')
debug(class_sizes)
# Scale features:
if args.features_scaling:
features_scaler = train_data.normalize_features()
val_data.normalize_features(features_scaler)
test_data.normalize_features(features_scaler)
else:
features_scaler = None
# Initialise scaler and scale training targets by subtracting mean and
# dividing standard deviation (regression only):
if args.dataset_type == 'regression':
debug('Fitting scaler')
scaler = StandardScaler()
targets = scaler.fit_transform(train_data.targets())
train_data.set_targets(targets)
else:
scaler = None
return train_data, val_data, test_data, scaler, features_scaler
def NUS_WIDE_load_three_party_data(data_dir,
selected_labels,
neg_label=-1,
n_samples=-1):
print("# load_three_party_data")
Xa, Xb, Xc, y = get_labeled_data_with_3_party(
data_dir=data_dir,
selected_labels=selected_labels,
n_samples=n_samples)
scale_model = StandardScaler()
Xa = scale_model.fit_transform(Xa)
Xb = scale_model.fit_transform(Xb)
Xc = scale_model.fit_transform(Xc)
y_ = []
pos_count = 0
neg_count = 0
for i in range(y.shape[0]):
# the first label in y as the first class while the other labels as the second class
if y[i, 0] == 1:
y_.append(1)
pos_count += 1
else:
y_.append(neg_label)
neg_count += 1
print("pos counts:", pos_count)
print("neg counts:", neg_count)
y = np.expand_dims(y_, axis=1)
n_train = int(0.8 * Xa.shape[0])
Xa_train, Xb_train, Xc_train = Xa[:n_train], Xb[:n_train], Xc[:n_train]
Xa_test, Xb_test, Xc_test = Xa[n_train:], Xb[n_train:], Xc[n_train:]
y_train, y_test = y[:n_train], y[n_train:]
print("Xa_train.shape:", Xa_train.shape)
print("Xb_train.shape:", Xb_train.shape)
print("Xc_train.shape:", Xc_train.shape)
print("Xa_test.shape:", Xa_test.shape)
print("Xb_test.shape:", Xb_test.shape)
print("Xc_test.shape:", Xc_test.shape)
print("y_train.shape:", y_train.shape)
print("y_test.shape:", y_test.shape)
return [Xa_train, Xb_train, Xc_train,
y_train], [Xa_test, Xb_test, Xc_test, y_test]
def test_scaler_without_centering():
rng = np.random.RandomState(42)
X = rng.randn(4, 5)
X[:, 0] = 0.0 # first feature is always of zero
X_csr = sparse.csr_matrix(X)
X_csc = sparse.csc_matrix(X)
assert_raises(ValueError, StandardScaler().fit, X_csr)
null_transform = StandardScaler(with_mean=False, with_std=False, copy=True)
X_null = null_transform.fit_transform(X_csr)
assert_array_equal(X_null.data, X_csr.data)
X_orig = null_transform.inverse_transform(X_null)
assert_array_equal(X_orig.data, X_csr.data)
scaler = StandardScaler(with_mean=False).fit(X)
X_scaled = scaler.transform(X, copy=True)
assert_false(np.any(np.isnan(X_scaled)))
scaler_csr = StandardScaler(with_mean=False).fit(X_csr)
X_csr_scaled = scaler_csr.transform(X_csr, copy=True)
assert_false(np.any(np.isnan(X_csr_scaled.data)))
scaler_csc = StandardScaler(with_mean=False).fit(X_csc)
X_csc_scaled = scaler_csr.transform(X_csc, copy=True)
assert_false(np.any(np.isnan(X_csc_scaled.data)))
assert_equal(scaler.mean_, scaler_csr.mean_)
assert_array_almost_equal(scaler.std_, scaler_csr.std_)
assert_equal(scaler.mean_, scaler_csc.mean_)
assert_array_almost_equal(scaler.std_, scaler_csc.std_)
assert_array_almost_equal(X_scaled.mean(axis=0),
[0., -0.01, 2.24, -0.35, -0.78], 2)
assert_array_almost_equal(X_scaled.std(axis=0), [0., 1., 1., 1., 1.])
X_csr_scaled_mean, X_csr_scaled_std = mean_variance_axis0(X_csr_scaled)
assert_array_almost_equal(X_csr_scaled_mean, X_scaled.mean(axis=0))
assert_array_almost_equal(X_csr_scaled_std, X_scaled.std(axis=0))
# Check that X has not been modified (copy)
assert_true(X_scaled is not X)
assert_true(X_csr_scaled is not X_csr)
X_scaled_back = scaler.inverse_transform(X_scaled)
assert_true(X_scaled_back is not X)
assert_true(X_scaled_back is not X_scaled)
assert_array_almost_equal(X_scaled_back, X)
X_csr_scaled_back = scaler_csr.inverse_transform(X_csr_scaled)
assert_true(X_csr_scaled_back is not X_csr)
assert_true(X_csr_scaled_back is not X_csr_scaled)
assert_array_almost_equal(X_csr_scaled_back.toarray(), X)
X_csc_scaled_back = scaler_csr.inverse_transform(X_csc_scaled.tocsc())
assert_true(X_csc_scaled_back is not X_csc)
assert_true(X_csc_scaled_back is not X_csc_scaled)
assert_array_almost_equal(X_csc_scaled_back.toarray(), X)
def test_scaler_without_centering():
rng = np.random.RandomState(42)
X = rng.randn(4, 5)
X[:, 0] = 0.0 # first feature is always of zero
X_csr = sparse.csr_matrix(X)
X_csc = sparse.csc_matrix(X)
assert_raises(ValueError, StandardScaler().fit, X_csr)
null_transform = StandardScaler(with_mean=False, with_std=False, copy=True)
X_null = null_transform.fit_transform(X_csr)
assert_array_equal(X_null.data, X_csr.data)
X_orig = null_transform.inverse_transform(X_null)
assert_array_equal(X_orig.data, X_csr.data)
scaler = StandardScaler(with_mean=False).fit(X)
X_scaled = scaler.transform(X, copy=True)
assert_false(np.any(np.isnan(X_scaled)))
scaler_csr = StandardScaler(with_mean=False).fit(X_csr)
X_csr_scaled = scaler_csr.transform(X_csr, copy=True)
assert_false(np.any(np.isnan(X_csr_scaled.data)))
scaler_csc = StandardScaler(with_mean=False).fit(X_csc)
X_csc_scaled = scaler_csr.transform(X_csc, copy=True)
assert_false(np.any(np.isnan(X_csc_scaled.data)))
assert_equal(scaler.mean_, scaler_csr.mean_)
assert_array_almost_equal(scaler.std_, scaler_csr.std_)
assert_equal(scaler.mean_, scaler_csc.mean_)
assert_array_almost_equal(scaler.std_, scaler_csc.std_)
assert_array_almost_equal(
X_scaled.mean(axis=0), [0., -0.01, 2.24, -0.35, -0.78], 2)
assert_array_almost_equal(X_scaled.std(axis=0), [0., 1., 1., 1., 1.])
X_csr_scaled_mean, X_csr_scaled_std = mean_variance_axis0(X_csr_scaled)
assert_array_almost_equal(X_csr_scaled_mean, X_scaled.mean(axis=0))
assert_array_almost_equal(X_csr_scaled_std, X_scaled.std(axis=0))
# Check that X has not been modified (copy)
assert_true(X_scaled is not X)
assert_true(X_csr_scaled is not X_csr)
X_scaled_back = scaler.inverse_transform(X_scaled)
assert_true(X_scaled_back is not X)
assert_true(X_scaled_back is not X_scaled)
assert_array_almost_equal(X_scaled_back, X)
X_csr_scaled_back = scaler_csr.inverse_transform(X_csr_scaled)
assert_true(X_csr_scaled_back is not X_csr)
assert_true(X_csr_scaled_back is not X_csr_scaled)
assert_array_almost_equal(X_csr_scaled_back.toarray(), X)
X_csc_scaled_back = scaler_csr.inverse_transform(X_csc_scaled.tocsc())
assert_true(X_csc_scaled_back is not X_csc)
assert_true(X_csc_scaled_back is not X_csc_scaled)
assert_array_almost_equal(X_csc_scaled_back.toarray(), X)
def main():
args = parse()
n_rollout = args.nrollout
n_epoch = args.epoch
savename = args.savename if args.savename is not None else 'model-' + str(
n_rollout) + 'unroll'
np.random.seed(1098)
path = args.filename
names = ['target_pos', 'target_speed', 'pos', 'vel', 'effort']
with h5py.File(path, 'r') as f:
(target_pos, target_speed, pos, vel,
effort) = [[np.array(val) for val in f[name].values()]
for name in names]
x_target = np.array(target_pos)
x_first = np.array([pos_[0] for pos_ in pos])
x_speed = np.array(target_speed).reshape((-1, 1))
aux_output = [np.ones(eff.shape[0]).reshape((-1, 1)) for eff in effort]
x = np.concatenate((x_target, x_first, x_speed), axis=1)
input_scaler = StandardScaler()
x = input_scaler.fit_transform(x)
output_scaler = StandardScaler()
effort_concat = np.concatenate([a for a in effort], axis=0)
output_scaler.fit(effort_concat)
effort = [output_scaler.transform(eff) for eff in effort]
y = pad_sequences(effort, padding='post', value=0.)
aux_output = pad_sequences(aux_output, padding='post', value=0.)
x, x_test, y, y_test, y_aux, y_aux_test = train_test_split(x,
y,
aux_output,
test_size=0.2)
y_mask, y_test_mask = [this_y[:, :, 0] for this_y in (y_aux, y_aux_test)]
y_aux_mask, y_aux_test_mask = [
np.ones(this_y.shape[:2]) for this_y in (y_aux, y_aux_test)
]
model = MyModel(train=[x, [y, y_aux]],
val=[x_test, [y_test, y_aux_test]],
train_mask=[y_mask, y_aux_mask],
val_mask=[y_test_mask, y_aux_test_mask],
max_unroll=n_rollout,
name=savename)
if not os.path.exists('save'):
os.makedirs('save')
if args.train:
model.fit(nb_epoch=n_epoch, batch_size=32)
elif args.resume:
model.resume(nb_epoch=n_epoch, batch_size=32)
def evalOne(parameters):
all_obs = []
all_pred = []
for location in locations:
trainX, testX, trainY, testY = splitDataForXValidation(location, "location", data, all_features, "target")
normalizer_X = StandardScaler()
trainX = normalizer_X.fit_transform(trainX)
testX = normalizer_X.transform(testX)
normalizer_Y = StandardScaler()
trainY = normalizer_Y.fit_transform(trainY)
testY = normalizer_Y.transform(testY)
model = BaggingRegressor(base_estimator=SVR(kernel='rbf', C=parameters["C"], cache_size=5000), max_samples=parameters["max_samples"],n_estimators=parameters["n_estimators"], verbose=0, n_jobs=-1)
model.fit(trainX, trainY)
prediction = model.predict(testX)
prediction = normalizer_Y.inverse_transform(prediction)
testY = normalizer_Y.inverse_transform(testY)
all_obs.extend(testY)
all_pred.extend(prediction)
return rmseEval(all_obs, all_pred)[1]
def test_scalar():
from sklearn.preprocessing.data import MinMaxScaler, StandardScaler
scalar = StandardScaler()
training = pd.read_csv(TRAIN_FEATURES_CSV, nrows=200000)
test = pd.read_csv(TEST_FEATURES_CSV)
# normalize the values
for column in TOTAL_TRAINING_FEATURE_COLUMNS:
training[column] = scalar.fit_transform(training[column])
test[column] = scalar.transform(test[column])
def evalOne(parameters):
all_obs = []
all_pred = []
for location in locations:
trainX, testX, trainY, testY = splitDataForXValidation(
location, "location", data, all_features, "target")
normalizer_X = StandardScaler()
trainX = normalizer_X.fit_transform(trainX)
testX = normalizer_X.transform(testX)
normalizer_Y = StandardScaler()
trainY = normalizer_Y.fit_transform(trainY)
testY = normalizer_Y.transform(testY)
layers = []
for _ in range(0, parameters["hidden_layers"]):
layers.append(
Layer(parameters["hidden_type"],
units=parameters["hidden_neurons"]))
layers.append(Layer("Linear"))
model = Regressor(layers=layers,
learning_rate=parameters["learning_rate"],
n_iter=parameters["iteration"],
random_state=42)
X = np.array(trainX)
y = np.array(trainY)
model.fit(X, y)
model.fit(trainX, trainY)
prediction = model.predict(testX)
prediction = normalizer_Y.inverse_transform(prediction)
testY = normalizer_Y.inverse_transform(testY)
print("location: " + str(location) + " -> " +
str(rmseEval(prediction, testY)[1]))
all_obs.extend(testY)
all_pred.extend(prediction)
return rmseEval(all_obs, all_pred)[1]
def retrieve_data(undersampling=False, ratio=1, random_state=None):
## Getting and reading csv-data files into a pandas dataframe
path = os.path.dirname(os.path.realpath(__file__))
file1 = path + "/../data/creditcard_part1.csv"
file2 = path + "/../data/creditcard_part2.csv"
df1 = pd.read_csv(file1)
df2 = pd.read_csv(file2)
df = pd.concat((df1, df2), ignore_index=True)
## Finding the class balances
class_counts = df.Class.value_counts()
num_fraudulent = class_counts[1]
num_non_fraudulent = class_counts[0]
## Splitting the dataset into design matrix X and targets y
X = df.loc[:, df.columns != 'Class'].values
y = df.loc[:, df.columns == 'Class'].values.ravel()
#### StandardScaler is more useful for classification, and Normalizer is more useful for regression.
standard_scaler = StandardScaler()
X = standard_scaler.fit_transform(X)
### Undersampling to fix imbalanced class
if undersampling:
if random_state is not None:
np.random.seed(random_state)
if ratio > 1:
raise ValueError("Undersampling ratio can't be larger than one")
multiplier = int(1.0 / ratio)
## Randomized undersampling method
indices_nonfraud = np.where(y == 0)[0]
indices_fraud = np.where(y == 1)[0]
np.random.shuffle(indices_nonfraud)
indices_nonfraud_under = indices_nonfraud[:multiplier * num_fraudulent]
indices_under = np.concatenate((indices_fraud, indices_nonfraud_under))
np.random.shuffle(indices_under)
## Using indices from undersampling method to create new balanced dataset
X_under = X[indices_under]
y_under = y[indices_under]
## Splitting the dataset into test and training sets
X_train, X_test, y_train, y_test = train_test_split(X_under,
y_under,
test_size=0.33,
random_state=4)
return X_train, X_test, y_train, y_test
def _proccess_input(self, target_pos, target_speed, pos, vel, effort):
x_target = np.array(target_pos)
x_first = np.array([pos_[0] for pos_ in pos])
x_speed = np.array(target_speed).reshape((-1, 1))
aux_output = [np.ones(eff.shape[0]).reshape((-1, 1)) for eff in effort]
x = np.concatenate((x_target, x_first, x_speed), axis=1)
input_scaler = StandardScaler()
x = input_scaler.fit_transform(x)
output_scaler = StandardScaler()
effort_concat = np.concatenate([a for a in effort], axis=0)
output_scaler.fit(effort_concat)
effort = [output_scaler.transform(eff) for eff in effort]
y = pad_sequences(effort, padding='post', value=0.)
aux_output = pad_sequences(aux_output, padding='post', value=0.)
x, x_test, y, y_test, y_aux, y_aux_test = train_test_split(x, y, aux_output, test_size=0.2)
return x, x_test, y, y_test, y_aux, y_aux_test
def test_iris(self):
train_X, test_X, train_y, test_y = data_io.get_iris_train_test()
print("train_X's shape = %s, train_y's shape = %s" %
(train_X.shape, train_y.shape))
print("test_X's shape = %s, test_y's shape = %s" %
(test_X.shape, test_y.shape))
print("Applying standard scaling ...")
scaler = StandardScaler()
train_X = scaler.fit_transform(train_X)
test_X = scaler.transform(test_X)
# train_X = test_X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
# train_y = test_y = np.array([0, 1, 1, 0])
# train_X = test_X = np.array([[0], [1]])
# train_y = test_y = np.array([0, 1])
layers = [100]
clf = MLPClassifier(layers,
batch_size=train_X.shape[0],
n_epochs=100,
learning_rate=0.1)
print("clf: %s" % clf)
print("Fitting ...")
clf.fit(train_X, train_y)
print("Predicting ...")
pred_y = clf.predict(test_X)
print("y = %s" % test_y)
print("pred_y = \n%s" % pred_y)
# pred_proba_y = clf.predict_proba(test_X)
# print("pred_proba_y = \n%s" % pred_proba_y)
accuracy = accuracy_score(test_y, pred_y)
print("Accuracy = %g%%" % (100 * accuracy))
self.assertGreaterEqual(accuracy, 0.89)
def test_scaler_int():
# test that scaler converts integer input to floating
# for both sparse and dense matrices
rng = np.random.RandomState(42)
X = rng.randint(20, size=(4, 5))
X[:, 0] = 0 # first feature is always of zero
X_csr = sparse.csr_matrix(X)
X_csc = sparse.csc_matrix(X)
null_transform = StandardScaler(with_mean=False, with_std=False, copy=True)
with warnings.catch_warnings(record=True):
X_null = null_transform.fit_transform(X_csr)
assert_array_equal(X_null.data, X_csr.data)
X_orig = null_transform.inverse_transform(X_null)
assert_array_equal(X_orig.data, X_csr.data)
with warnings.catch_warnings(record=True):
scaler = StandardScaler(with_mean=False).fit(X)
X_scaled = scaler.transform(X, copy=True)
assert_false(np.any(np.isnan(X_scaled)))
with warnings.catch_warnings(record=True):
scaler_csr = StandardScaler(with_mean=False).fit(X_csr)
X_csr_scaled = scaler_csr.transform(X_csr, copy=True)
assert_false(np.any(np.isnan(X_csr_scaled.data)))
with warnings.catch_warnings(record=True):
scaler_csc = StandardScaler(with_mean=False).fit(X_csc)
X_csc_scaled = scaler_csr.transform(X_csc, copy=True)
assert_false(np.any(np.isnan(X_csc_scaled.data)))
assert_equal(scaler.mean_, scaler_csr.mean_)
assert_array_almost_equal(scaler.std_, scaler_csr.std_)
assert_equal(scaler.mean_, scaler_csc.mean_)
assert_array_almost_equal(scaler.std_, scaler_csc.std_)
assert_array_almost_equal(
X_scaled.mean(axis=0),
[0., 1.109, 1.856, 21., 1.559], 2)
assert_array_almost_equal(X_scaled.std(axis=0), [0., 1., 1., 1., 1.])
X_csr_scaled_mean, X_csr_scaled_std = mean_variance_axis0(
X_csr_scaled.astype(np.float))
assert_array_almost_equal(X_csr_scaled_mean, X_scaled.mean(axis=0))
assert_array_almost_equal(X_csr_scaled_std, X_scaled.std(axis=0))
# Check that X has not been modified (copy)
assert_true(X_scaled is not X)
assert_true(X_csr_scaled is not X_csr)
X_scaled_back = scaler.inverse_transform(X_scaled)
assert_true(X_scaled_back is not X)
assert_true(X_scaled_back is not X_scaled)
assert_array_almost_equal(X_scaled_back, X)
X_csr_scaled_back = scaler_csr.inverse_transform(X_csr_scaled)
assert_true(X_csr_scaled_back is not X_csr)
assert_true(X_csr_scaled_back is not X_csr_scaled)
assert_array_almost_equal(X_csr_scaled_back.toarray(), X)
X_csc_scaled_back = scaler_csr.inverse_transform(X_csc_scaled.tocsc())
assert_true(X_csc_scaled_back is not X_csc)
assert_true(X_csc_scaled_back is not X_csc_scaled)
assert_array_almost_equal(X_csc_scaled_back.toarray(), X)
def main():
df = load_train_data()
logger.info('column hash = %d', utils.column_hash(df))
df = preprocess.drop_column(df, 'fullVisitorId')
df = preprocess.drop_column(df, 'sessionId')
# debug_info(df)
y = df['totals_transactionRevenue']
X = preprocess.drop_column(df, 'totals_transactionRevenue')
# X, _, y, _ = utils.split_data(X, y, ratio=0.9, seed=42)
# n_classes = 10
n_models = 100
y_max = y.max()
for i in range(n_models):
X_train, X_test, y_train, y_test = utils.split_data(X, y)
logger.info('training')
# y_train, quants = preprocess.make_class_target(y_train, n_classes)
# logger.info('y_train.unique() = %s', y_train.unique())
# logger.info('quants = %s', quants)
# y_train = preprocess.make_class_target2(y_train, y_max, n_classes)
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
logger.info('X_train.shape = %s', X_train.shape)
# cumulative = np.cumsum(pca.explained_variance_ratio_)
# pylab.plot(cumulative, 'r-')
# pylab.show()
# model = build_classifier(X_train.shape[1], n_classes)
model = build_regressor(X_train.shape[1])
EPOCHS = 100
early_stop = tf.keras.callbacks.EarlyStopping(monitor='val_loss',
patience=5)
history = model.fit(X_train,
y_train,
epochs=EPOCHS,
validation_split=0.1,
verbose=0,
callbacks=[early_stop,
utils.EpochCallback()])
linear_model = LinearRegression()
linear_model.fit(X_train, y_train)
logger.info('predicting')
logger.info('X_test.shape = %s', X_test.shape)
X_test = scaler.transform(X_test)
# y_classes = model.predict(X_test)
# y_pred = postprocess.make_real_predictions(y_classes, quants)
# y_pred = postprocess.make_real_predictions2(y_classes, y_max)
y_pred = model.predict(X_test).flatten()
y_linear_pred = linear_model.predict(X_test)
rms = np.sqrt(mean_squared_error(y_test, y_pred))
linear_rms = np.sqrt(mean_squared_error(y_test, y_linear_pred))
logger.info('rms = %s', rms)
logger.info('linear_rms = %s', linear_rms)
# save_model(model, i, quants, scaler)
save_model2(model, linear_model, i, y_max, scaler)
# plot_history_classifier(history)
plot_history_regressor(history)
pylab.figure()
pylab.scatter(y_pred, y_test, alpha=0.5)
pylab.xlabel("pred")
pylab.ylabel("test")
hist_revenue(y_linear_pred, 'y_linear_pred')
hist_revenue(y_pred, 'y_pred')
hist_revenue(y_test, 'y_test')
pylab.show()
columns = []
loadData("/data/york_hour_2013.csv", ["timestamp", "atc"], data, columns)
all_features = deepcopy(columns)
all_features.remove("target")
all_features.remove("location")
output = open(OUTPUT_DATA_FILE, 'w')
output.write("location,observation,prediction\n")
for location in locations:
print(str(location))
trainX, testX, trainY, testY = splitDataForXValidation(
location, "location", data, all_features, "target")
normalizer_X = StandardScaler()
trainX = normalizer_X.fit_transform(trainX)
testX = normalizer_X.transform(testX)
normalizer_Y = StandardScaler()
trainY = normalizer_Y.fit_transform(trainY)
testY = normalizer_Y.transform(testY)
model = BaggingRegressor(base_estimator=SVR(kernel='rbf',
C=40,
cache_size=5000),
max_samples=4200,
n_estimators=10,
verbose=0,
n_jobs=-1)
model.fit(trainX, trainY)
prediction = model.predict(testX)
prediction = normalizer_Y.inverse_transform(prediction)
testY = normalizer_Y.inverse_transform(testY)
X2 = [[float(x) / 10.0, float(x) / 10.0,
float(x) / 10.0] for x in range(0, 231)]
layers = []
layers.append(Layer("Rectifier", units=100))
layers.append(Layer("Rectifier", units=100))
layers.append(Layer("Rectifier", units=100))
layers.append(Layer("Linear"))
model = Regressor(layers=layers,
learning_rate=0.001,
n_iter=5000,
random_state=42)
normalizer_X = StandardScaler()
trainX = normalizer_X.fit_transform(X)
trainX2 = normalizer_X.fit_transform(X2)
normalizer_Y = StandardScaler()
trainY = normalizer_Y.fit_transform(Y)
model.fit(np.array(trainX), np.array(trainY))
Y_pred = model.predict(np.array(trainX2))
Y_pred = normalizer_Y.inverse_transform(Y_pred)
Y_pred = [y[0] for y in Y_pred]
print(str(Y_pred))
plot2(Y, Y_pred, OUTPUT_PNG_FILE, "Observed pollution concentration levels",
"Predicted pollution concentration levels by ANR")
bestPar2 = clf1.best_params_
writeDict(str(bestPar2), "dict2XGB.txt")
else:
clf1 = XGBClassifier(**bestParamXGB2)
clf1.fit(dataTrain, y)
delta = gmtime(time() - t0)
tstr = strftime('%H:%M:%S', delta)
print("Time since beginning:%s" % tstr)
# Scale data
print("Scaling")
scaler = StandardScaler()
imputer = Imputer()
dataTrain = imputer.fit_transform(dataTrain)
dataTrain = scaler.fit_transform(dataTrain)
#Fit classifier that needs imputation
print("Fitting")
if (gridSearchRF):
clf2.fit(dataTrain, y)
bestPar2 = clf2.best_params_
writeDict(str(bestPar2), "dictRF.txt")
else:
clf2 = XGBClassifier(**bestParamRF)
clf2.fit(dataTrain, y)
print("Fit completed")
delta = gmtime(time() - t0)
tstr = strftime('%H:%M:%S', delta)
plt.ylabel("Amount", size=14)
plt.show()
## Plotting the correlation matrix. (Dataset is already PCA'd)
sb.heatmap(data=df.corr(), cmap="viridis", annot=False)
plt.show()
## There are no categories in the dataset, so no need to do one-hot encoding.
## Splitting the dataset into design matrix X and targets y
X = df.loc[:, df.columns != 'Class'].values
y = df.loc[:, df.columns == 'Class'].values.ravel()
## Scaling the data (Most for Time and Amount)
standard_scaler = StandardScaler()
X = standard_scaler.fit_transform(X)
## Randomized undersampling method
indices_nonfraud = np.where(y == 0)[0]
indices_fraud = np.where(y == 1)[0]
np.random.shuffle(indices_nonfraud)
indices_nonfraud_under = indices_nonfraud[:num_fraudulent]
indices_under = np.concatenate((indices_fraud, indices_nonfraud_under))
np.random.shuffle(indices_under)
## Using indices from undersampling method to create new balanced dataset
X_under = X[indices_under]
y_under = y[indices_under]
## Looking at the class balance again, now for undersampled data
plt.bar([0, 1], [len(indices_nonfraud_under), len(indices_fraud)])
tf.keras.metrics.AUC(name='auc')
])
save_best_callback = tf.keras.callbacks.ModelCheckpoint(
'./model-{epoch:02d}-{acc:.2f}.hdf5',
monitor='acc',
verbose=1,
save_best_only=True,
save_weights_only=False,
save_freq=1)
logdir = os.path.join('tflogs',
datetime.datetime.now().strftime('%Y%m%d-%H%M%S'))
tb_train_callback = tf.keras.callbacks.TensorBoard(logdir,
histogram_freq=1,
profile_batch=0)
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
model.fit(
X_train_scaled,
y_train,
class_weight=class_weight,
# batch_size=64,
validation_split=0.1,
callbacks=[save_best_callback, tb_train_callback],
epochs=50)
# model = tf.keras.models.load_model('./model-35-0.88.hdf5')
X_test_scaled = scaler.transform(X_test)
model.evaluate(X_test_scaled, y_test)
# print(np.round(model.predict(X_test)))
def main():
args = parse()
n_rollout = args.nrollout
n_epoch = args.epoch
seed = 124
np.random.seed(seed)
path = args.filename
names = ['target_pos', 'target_speed', 'pos', 'vel', 'effort']
with h5py.File(path, 'r') as f:
(target_pos, target_speed, pos, vel,
effort) = [[np.array(val) for val in f[name_].values()]
for name_ in names]
x_target = np.array(target_pos)
x_first = np.array([pos_[0] for pos_ in pos])
v_first = np.array([vel_[0] for vel_ in vel])
x_speed = np.array(target_speed).reshape((-1, 1))
aux_output = [np.ones(eff_.shape[0]).reshape((-1, 1)) for eff_ in effort]
x = np.concatenate((x_target, x_first, v_first, x_speed), axis=1)
def prepare_time_data(data):
data_scaler = StandardScaler()
data_concat = np.concatenate(data, axis=0)
data_scaler.fit(data_concat)
new_data = [data_scaler.transform(data_) for data_ in data]
return data_scaler, new_data
input_scaler = StandardScaler()
x = input_scaler.fit_transform(x)
effort_scaler, effort = prepare_time_data(effort)
pos_scaler, pos = prepare_time_data(pos)
vel_scaler, vel = prepare_time_data(vel)
torque = pad_sequences(effort, padding='post', value=0., dtype=np.float64)
pos = pad_sequences(pos, padding='post', value=0., dtype=np.float64)
vel = pad_sequences(vel, padding='post', value=0., dtype=np.float64)
aux_output = pad_sequences(aux_output,
padding='post',
value=0.,
dtype=np.float64)
mask = aux_output[:, :, 0]
aux_mask = np.ones(aux_output.shape[:2])
x, x_test, torque, torque_test, pos, pos_test, vel, vel_test, \
aux, aux_test, mask, mask_test, aux_mask, aux_mask_test = \
train_test_split(x, torque, pos, vel, aux_output, mask, aux_mask, test_size=0.3, random_state=seed)
kf = KFold(n_splits=3, shuffle=True, random_state=seed)
if not os.path.exists('save_model_selection'):
os.makedirs('save_model_selection')
for (train_index, cv_index), i in zip(kf.split(x), range(kf.n_splits)):
widths_gru = [1000]
depths_gru = [1]
dropout_fractions = [0.5]
convolution_layer = [False]
l2_weights = [1e-3]
names = [
'gru:{}-{}_conv:{}_dropout:{}_l2:{}/fold:{}'.format(
width_, depth_, conv_, drop_, l2_, i)
for width_, depth_, conv_, drop_, l2_ in zip(
widths_gru, depths_gru, convolution_layer, dropout_fractions,
l2_weights)
]
save_names = ['save_model_selection/' + name_ for name_ in names]
log_names = ['log_model_selection/' + name_ for name_ in names]
img_names = ['imgs/' + name_ for name_ in names]
this_x, this_torque, this_pos, this_vel, this_aux, this_mask, this_aux_mask = \
[a_[train_index] for a_ in [x, torque, pos, vel, aux, mask, aux_mask]]
this_x_cv, this_torque_cv, this_pos_cv, this_vel_cv, this_aux_cv, this_mask_cv, this_aux_mask_cv = \
[a_[cv_index] for a_ in [x, torque, pos, vel, aux, mask, aux_mask]]
for width_gru, depth_gru, dropout_fraction, conv, l2_weight, save_name, log_name, img_name in \
zip(widths_gru, depths_gru, dropout_fractions, convolution_layer, l2_weights,
save_names, log_names, img_names):
div_torque = np.split(this_torque, 7, axis=2)
div_torque_cv = np.split(this_torque_cv, 7, axis=2)
div_torque_test = np.split(torque_test, 7, axis=2)
model = MyModel(train=[this_x, div_torque + [this_aux]],
val=[this_x_cv, div_torque_cv + [this_aux_cv]],
test=[x_test, div_torque_test + [aux_test]],
train_mask=[this_mask] * 7 + [this_aux_mask],
val_mask=[this_mask_cv] * 7 + [this_aux_mask_cv],
test_mask=[mask_test] * 7 + [aux_mask_test],
max_unroll=n_rollout,
save_dir=save_name,
log_dir=log_name,
img_dir=img_name,
width_gru=width_gru,
depth_gru=depth_gru,
width_dense=50,
depth_dense=2,
torque_scaler=effort_scaler,
conv=conv,
dropout_fraction=dropout_fraction,
l2_weight=l2_weight)
if args.train:
model.fit(nb_epoch=n_epoch, batch_size=512)
elif args.resume:
model.resume(nb_epoch=n_epoch, batch_size=512)
if args.visualization:
model.load()
model.save_figs()
def test_scaler_int():
# test that scaler converts integer input to floating
# for both sparse and dense matrices
rng = np.random.RandomState(42)
X = rng.randint(20, size=(4, 5))
X[:, 0] = 0 # first feature is always of zero
X_csr = sparse.csr_matrix(X)
X_csc = sparse.csc_matrix(X)
null_transform = StandardScaler(with_mean=False, with_std=False, copy=True)
with warnings.catch_warnings(record=True):
X_null = null_transform.fit_transform(X_csr)
assert_array_equal(X_null.data, X_csr.data)
X_orig = null_transform.inverse_transform(X_null)
assert_array_equal(X_orig.data, X_csr.data)
with warnings.catch_warnings(record=True):
scaler = StandardScaler(with_mean=False).fit(X)
X_scaled = scaler.transform(X, copy=True)
assert_false(np.any(np.isnan(X_scaled)))
with warnings.catch_warnings(record=True):
scaler_csr = StandardScaler(with_mean=False).fit(X_csr)
X_csr_scaled = scaler_csr.transform(X_csr, copy=True)
assert_false(np.any(np.isnan(X_csr_scaled.data)))
with warnings.catch_warnings(record=True):
scaler_csc = StandardScaler(with_mean=False).fit(X_csc)
X_csc_scaled = scaler_csr.transform(X_csc, copy=True)
assert_false(np.any(np.isnan(X_csc_scaled.data)))
assert_equal(scaler.mean_, scaler_csr.mean_)
assert_array_almost_equal(scaler.std_, scaler_csr.std_)
assert_equal(scaler.mean_, scaler_csc.mean_)
assert_array_almost_equal(scaler.std_, scaler_csc.std_)
assert_array_almost_equal(X_scaled.mean(axis=0),
[0., 1.109, 1.856, 21., 1.559], 2)
assert_array_almost_equal(X_scaled.std(axis=0), [0., 1., 1., 1., 1.])
X_csr_scaled_mean, X_csr_scaled_std = mean_variance_axis0(
X_csr_scaled.astype(np.float))
assert_array_almost_equal(X_csr_scaled_mean, X_scaled.mean(axis=0))
assert_array_almost_equal(X_csr_scaled_std, X_scaled.std(axis=0))
# Check that X has not been modified (copy)
assert_true(X_scaled is not X)
assert_true(X_csr_scaled is not X_csr)
X_scaled_back = scaler.inverse_transform(X_scaled)
assert_true(X_scaled_back is not X)
assert_true(X_scaled_back is not X_scaled)
assert_array_almost_equal(X_scaled_back, X)
X_csr_scaled_back = scaler_csr.inverse_transform(X_csr_scaled)
assert_true(X_csr_scaled_back is not X_csr)
assert_true(X_csr_scaled_back is not X_csr_scaled)
assert_array_almost_equal(X_csr_scaled_back.toarray(), X)
X_csc_scaled_back = scaler_csr.inverse_transform(X_csc_scaled.tocsc())
assert_true(X_csc_scaled_back is not X_csc)
assert_true(X_csc_scaled_back is not X_csc_scaled)
assert_array_almost_equal(X_csc_scaled_back.toarray(), X)
class SkRanker(Ranker, SkLearner):
'''
Basic ranker wrapping scikit-learn functions
'''
def train(self, dataset_filename,
scale=True,
feature_selector=None,
feature_selection_params={},
feature_selection_threshold=.25,
learning_params={},
optimize=True,
optimization_params={},
scorers=['f1_score'],
attribute_set=None,
class_name=None,
metaresults_prefix="./0-",
**kwargs):
plot_filename = "{}{}".format(metaresults_prefix, "featureselection.pdf")
data, labels = dataset_to_instances(dataset_filename, attribute_set, class_name, **kwargs)
learner = self.learner
#the class must remember the attribute_set and the class_name in order to reproduce the vectors
self.attribute_set = attribute_set
self.class_name = class_name
#scale data to the mean
if scale:
log.info("Scaling datasets...")
log.debug("Data shape before scaling: {}".format(data.shape))
self.scaler = StandardScaler()
data = self.scaler.fit_transform(data)
log.debug("Data shape after scaling: {}".format(data.shape))
log.debug("Mean: {} , Std: {}".format(self.scaler.mean_, self.scaler.std_))
#avoid any NaNs and Infs that may have occurred due to the scaling
data = np.nan_to_num(data)
#feature selection
if isinstance(feature_selection_params, basestring):
feature_selection_params = eval(feature_selection_params)
self.featureselector, data, metadata = self.run_feature_selection(data, labels, feature_selector, feature_selection_params, feature_selection_threshold, plot_filename)
#initialize learning method and scoring functions and optimize
self.learner, self.scorers = self.initialize_learning_method(learner, data, labels, learning_params, optimize, optimization_params, scorers)
log.info("Data shape before fitting: {}".format(data.shape))
self.learner.fit(data, labels)
self.fit = True
return metadata
def get_model_description(self):
params = {}
if self.scaler:
params = self.scaler.get_params(deep=True)
try: #these are for SVC
if self.learner.kernel == "rbf":
params["gamma"] = self.learner.gamma
params["C"] = self.learner.C
for i, n_support in enumerate(self.learner.n_support_):
params["n_{}".format(i)] = n_support
log.debug(len(self.learner.dual_coef_))
return params
elif self.learner.kernel == "linear":
coefficients = self.learner.coef_
att_coefficients = {}
for attname, coeff in zip(self.attribute_set.get_names_pairwise(), coefficients[0]):
att_coefficients[attname] = coeff
return att_coefficients
except AttributeError:
pass
try: #adaboost etc
params = self.learner.get_params()
numeric_params = OrderedDict()
for key, value in params.iteritems():
try:
value = float(value)
except ValueError:
continue
numeric_params[key] = value
return numeric_params
except:
pass
return {}
def get_ranked_sentence(self, parallelsentence, critical_attribute="rank_predicted",
new_rank_name="rank_hard",
del_orig_class_att=False,
bidirectional_pairs=False,
ties=True,
reconstruct='hard'):
"""
"""
if type(self.learner) == str:
if self.classifier:
self.learner = self.classifier
# this is to provide backwards compatibility for old models
# whose classes used differeent attribute names
try:
self.learner._dual_coef_ = self.learner.dual_coef_
self.learner._intercept_ = self.learner.intercept_
except AttributeError:
# it's ok if the model doesn't have these variables
pass
try: # backwards compatibility for old LogisticRegression
try_classes = self.learner.classes_
except AttributeError:
self.learner.classes_ = [-1, 1]
#de-compose multiranked sentence into pairwise comparisons
pairwise_parallelsentences = parallelsentence.get_pairwise_parallelsentences(bidirectional_pairs=bidirectional_pairs,
class_name=self.class_name,
ties=ties)
if len(parallelsentence.get_translations()) == 1:
log.warning("Parallelsentence has only one target sentence")
parallelsentence.tgt[0].add_attribute(new_rank_name, 1)
return parallelsentence, {}
elif len(parallelsentence.get_translations()) == 0:
return parallelsentence, {}
#list that will hold the pairwise parallel sentences including the learner's decision
classified_pairwise_parallelsentences = []
resultvector = {}
for pairwise_parallelsentence in pairwise_parallelsentences:
#convert pairwise parallel sentence into an orange instance
instance = parallelsentence_to_instance(pairwise_parallelsentence, attribute_set=self.attribute_set)
#scale data instance to mean, based on trained scaler
if self.scaler:
try:
instance = np.nan_to_num(instance)
instance = self.scaler.transform(instance)
except ValueError as e:
log.error("Could not transform instance: {}, scikit replied: {}".format(instance, e))
#raise ValueError(e)
pass
try:
if self.featureselector:
instance = np.nan_to_num(instance)
instance = self.featureselector.transform(instance)
except AttributeError:
pass
log.debug('Instance = {}'.format(instance))
#make sure no NaN or inf appears in the instance
instance = np.nan_to_num(instance)
#run learner for this instance
predicted_value = self.learner.predict(instance)
try:
distribution = dict(zip(self.learner.classes_, self.learner.predict_proba(instance)[0]))
except AttributeError:
#if learner does not support per-class probability (e.g. LinearSVC) assign 0.5
distribution = dict([(cl, 0.5) for cl in self.learner.classes_])
log.debug("Distribution: {}".format(distribution))
log.debug("Predicted value: {}".format(predicted_value))
#even if we have a binary learner, it may be that it cannot decide between two classes
#for us, this means a tie
if not bidirectional_pairs and distribution and len(distribution)==2 and float(distribution[1])==0.5:
predicted_value = 0
distribution[predicted_value] = 0.5
log.debug("{}, {}, {}".format(pairwise_parallelsentence.get_system_names(), predicted_value, distribution))
#gather several metadata from the classification, which may be needed
resultvector.update({'systems' : pairwise_parallelsentence.get_system_names(),
'value' : predicted_value,
'distribution': distribution,
'confidence': distribution[int(predicted_value)],
# 'instance' : instance,
})
#add the new predicted ranks as attributes of the new pairwise sentence
pairwise_parallelsentence.add_attributes({"rank_predicted":predicted_value,
"prob_-1":distribution[-1],
"prob_1":distribution[1]
})
classified_pairwise_parallelsentences.append(pairwise_parallelsentence)
#gather all classified pairwise comparisons of into one parallel sentence again
sentenceset = CompactPairwiseParallelSentenceSet(classified_pairwise_parallelsentences)
if reconstruct == 'hard':
log.debug("Applying hard reconstruction to produce rank {}".format(new_rank_name))
ranked_sentence = sentenceset.get_multiranked_sentence(critical_attribute=critical_attribute,
new_rank_name=new_rank_name,
del_orig_class_att=del_orig_class_att)
else:
attribute1 = "prob_-1"
attribute2 = "prob_1"
log.debug("Applying soft reconstruction to produce rank {}".format(new_rank_name))
try:
ranked_sentence = sentenceset.get_multiranked_sentence_with_soft_ranks(attribute1, attribute2,
critical_attribute, new_rank_name, normalize_ranking=False)
except:
raise ValueError("Sentenceset {} from {} caused exception".format(classified_pairwise_parallelsentences, parallelsentence))
return ranked_sentence, resultvector
svecs.append(svec)
#END per-student loop
svecs = numpy.array(svecs)
# gmarks = []
# for sv in svecs:
# if sv[0]==-1:
# gmarks.append("D")
# elif sv[0]==1:
# gmarks.append("O")
# else:
# gmarks.append(".")
scaler = StandardScaler()
svecs = scaler.fit_transform(svecs)
print(svecs)
print(m, f, h)
print("number of students", len(svecs))
print("gender, avg_atts, avg_hints, avg_succ, avg_diff")
kmeans = KMeans(n_clusters=3, n_jobs=-1).fit(svecs)
labs = kmeans.labels_
centres = kmeans.cluster_centers_
print(centres)
print(kmeans.inertia_)
pca = PCA(n_components=2)
tvecs = pca.fit_transform(svecs)
x, y = zip(*tvecs)
def train_and_test(alpha,
predictors,
predictor_params,
x_filename,
y_filename,
n_users,
percTest,
featureset_to_use,
diff_weighting,
phi,
force_balanced_classes,
do_scaling,
optimise_predictors,
report,
conf_report=None):
# all_X = numpy.loadtxt(x_filename, delimiter=",")
all_X = numpy.load(x_filename + ".npy")
all_y = numpy.loadtxt(y_filename, delimiter=",")
print("loaded X and y files", x_filename, y_filename)
if numpy.isnan(all_X.any()):
print("nan in", x_filename)
exit()
if numpy.isnan(all_y.any()):
print("nan in", y_filename)
exit()
#print("selecting balanced subsample")
print("t t split")
X_train, X_test, y_train, y_test = train_test_split(all_X,
all_y,
test_size=percTest,
random_state=666)
# feature extraction
# test = SelectKBest(score_func=chi2, k=100)
# kb = test.fit(X_train, y_train)
# # summarize scores
# numpy.set_printoptions(precision=3)
# print(kb.scores_)
# features = kb.transform(X_train)
# mask = kb.get_support()
# # summarize selected features
# print(features.shape)
# X_train = X_train[:,mask]
# X_test = X_test[:,mask]
scaler = StandardScaler()
rdim = FeatureAgglomeration(n_clusters=100)
if do_scaling:
# input(X_train.shape)
X_train = rdim.fit_transform(X_train)
X_test = rdim.transform(X_test)
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
with open('../../../isaac_data_files/qutor_scaler.pkl',
'wb') as output:
pickle.dump(scaler, output, pickle.HIGHEST_PROTOCOL)
with open('../../../isaac_data_files/qutor_rdim.pkl', 'wb') as output:
pickle.dump(rdim, output, pickle.HIGHEST_PROTOCOL)
# print("feature reduction...")
# pc = PCA(n_components=100)
# X_train = pc.fit_transform(X_train)
# X_test = pc.transform(X_test)
classes = numpy.unique(y_train)
sample_weights = None
if (force_balanced_classes):
X_train, y_train = balanced_subsample(X_train, y_train, 1.0) #0.118)
print("X_train shape:", X_train.shape)
print("X_test shape:", X_test.shape)
print("tuning classifier ...")
for ix, p in enumerate(predictors):
print(type(p))
print(p.get_params().keys())
if optimise_predictors == True and len(predictor_params[ix]) > 1:
pbest = run_random_search(p, X_train, y_train,
predictor_params[ix])
else:
pbest = p.fit(X_train, y_train)
predictors[ix] = pbest
print("pickling classifier ...")
for ix, p in enumerate(predictors):
p_name = predictor_params[ix]['name']
with open(
'../../../isaac_data_files/p_{}_{}_{}.pkl'.format(
p_name, alpha, phi), 'wb') as output:
pickle.dump(p, output, pickle.HIGHEST_PROTOCOL)
print("done!")
# report.write("* ** *** |\| \` | | |) /; `|` / |_| *** ** *\n")
# report.write("* ** *** | | /_ |^| |) || | \ | | *** ** *\n")
#report.write("RUNS,P,FB,WGT,ALPHA,PHI,SCL,0p,0r,0F,0supp,1p,1r,1F,1supp,avg_p,avg_r,avg_F,#samples\n")
for ix, p in enumerate(predictors):
report.write(",".join(
map(str, (all_X.shape[0], str(p).replace(",", ";").replace(
"\n", ""), force_balanced_classes, diff_weighting, alpha, phi,
do_scaling))))
y_pred_tr = p.predict(X_train)
y_pred = p.predict(X_test)
# for x,y,yp in zip(X_train, y_test, y_pred):
if conf_report:
conf_report.write(
str(p).replace(",", ";").replace("\n", "") + "\n")
conf_report.write(str(alpha) + "," + str(phi) + "\n")
conf_report.write(str(confusion_matrix(y_test, y_pred)) + "\n")
conf_report.write("\n")
# p = precision_score(y_test, y_pred, average=None, labels=classes)
# r = recall_score(y_test, y_pred, average=None, labels=classes)
# F = f1_score(y_test, y_pred, average=None, labels=classes)
p, r, F, s = precision_recall_fscore_support(y_test,
y_pred,
labels=classes,
average=None,
warn_for=('precision',
'recall',
'f-score'))
avp, avr, avF, _ = precision_recall_fscore_support(
y_test,
y_pred,
labels=classes,
average='weighted',
warn_for=('precision', 'recall', 'f-score'))
for ix, c in enumerate(classes):
report.write(",{},{},{},{},{},".format(c, p[ix], r[ix], F[ix],
s[ix]))
report.write("{},{},{},{}\n".format(avp, avr, avF, numpy.sum(s)))
# report.write(classification_report(y_test, y_pred)+"\n")
# report.write("------END OF CLASSIFIER------\n")
report.flush()
return X_train, X_test, y_pred_tr, y_pred, y_test, scaler
def split_train_validation_test(multi_time_series_df,
valid_start_time,
test_start_time,
features,
time_step_lag=1,
horizon=1,
target='target',
time_format='%Y-%m-%d %H:%M:%S',
freq='H'):
if not isinstance(features, list) or len(features) < 1:
raise Exception(
"Bad input for features. It must be an array of dataframe colummns used"
)
train = multi_time_series_df.copy()[
multi_time_series_df.index < valid_start_time]
train_features = train[features]
train_targets = train[target]
# X_scaler = MinMaxScaler()
# target_scaler = MinMaxScaler()
# y_scaler = MinMaxScaler()
X_scaler = StandardScaler()
target_scaler = StandardScaler()
y_scaler = StandardScaler()
# 'load' is our key target. If it is in features, then we scale it.
# if it not 'load', then we scale the first column
if 'load' in features:
tg = train[['load']]
y_scaler.fit(tg)
else:
tg = train[target]
## scale the first column
y_scaler.fit(tg.values.reshape(-1, 1))
train[target] = target_scaler.fit_transform(train_targets)
X_scaler.fit(train_features)
train[features] = X_scaler.transform(train_features)
tensor_structure = {'X': (range(-time_step_lag + 1, 1), features)}
train_inputs = TimeSeriesTensor(train,
target=target,
H=horizon,
freq=freq,
tensor_structure=tensor_structure)
print(train_inputs.dataframe.head())
look_back_dt = dt.datetime.strptime(
valid_start_time, time_format) - dt.timedelta(hours=time_step_lag - 1)
valid = multi_time_series_df.copy()[
(multi_time_series_df.index >= look_back_dt)
& (multi_time_series_df.index < test_start_time)]
valid_features = valid[features]
valid[features] = X_scaler.transform(valid_features)
tensor_structure = {'X': (range(-time_step_lag + 1, 1), features)}
valid_inputs = TimeSeriesTensor(valid,
target=target,
H=horizon,
freq=freq,
tensor_structure=tensor_structure)
print(valid_inputs.dataframe.head())
# test set
# look_back_dt = dt.datetime.strptime(test_start_time, '%Y-%m-%d %H:%M:%S') - dt.timedelta(hours=time_step_lag - 1)
test = multi_time_series_df.copy()[test_start_time:]
test_features = test[features]
test[features] = X_scaler.transform(test_features)
test_inputs = TimeSeriesTensor(test,
target=target,
H=horizon,
freq=freq,
tensor_structure=tensor_structure)
print("time lag:", time_step_lag, "original_feature:", len(features))
return train_inputs, valid_inputs, test_inputs, y_scaler
