class ROCKET():
def __init__(self, num_kernels=100):
self.num_kernels = num_kernels
def train(self, X_train, Y_train):
_ = generate_kernels(100, 10)
apply_kernels(np.zeros_like(X_train)[:, 1:], _)
input_length = X_train.shape[1]
self.kernels = generate_kernels(input_length, self.num_kernels)
X_transform = apply_kernels(X_train, self.kernels)
self.classifier = RidgeClassifierCV(alphas=10**np.linspace(-3, 3, 10),
normalize=True)
return self.classifier.fit(X_transform, Y_train)
def test(self, X_test, Y_test):
X_transform = apply_kernels(X_test, self.kernels)
results = self.classifier.score(X_transform, Y_test)
return results
def classify(train, test):
X, y = train.iloc[:, 0:-1], train.iloc[:, -1]
clf = RidgeClassifierCV(alphas=np.logspace(-3, 3, 10),
normalize=True).fit(X, y)
preds = clf.predict(test.iloc[:, 0:-1])
return accuracy_score(preds, test.iloc[:, -1])
def linear_models(x_train, y_train):
from sklearn.linear_model import LogisticRegression
classifier1 = LogisticRegression(C=1.2, random_state=0, max_iter=1500)
classifier1.fit(x_train, y_train)
from sklearn.linear_model import PassiveAggressiveClassifier
classifier2 = PassiveAggressiveClassifier()
classifier2.fit(x_train, y_train)
from sklearn.linear_model import RidgeClassifierCV
classifier3 = RidgeClassifierCV()
classifier3.fit(x_train, y_train)
from sklearn.linear_model import SGDClassifier
classifier4 = SGDClassifier()
classifier4.fit(x_train, y_train)
from sklearn.linear_model import Perceptron
classifier5 = Perceptron()
classifier5.fit(x_train, y_train)
print('LogisticRegression training accuracy: ',
classifier1.score(x_train, y_train))
print('PassiveAggressiveClassifier training accuracy: ',
classifier2.score(x_train, y_train))
print('RidgeClassifierCV training accuracy: ',
classifier3.score(x_train, y_train))
print('SGDClassifier training accuracy: ',
classifier4.score(x_train, y_train))
print('Perceptron training accuracy: ',
classifier5.score(x_train, y_train))
return classifier1, classifier2, classifier3, classifier4, classifier5
def run(train_file, test_file, num_seq, n_jobs):
print("Load train data")
y, s = load_data(train_file)
print("Generate random features")
ss = generate_features(s, num_seq)
print("Generate automaton")
A = ahocorasick.Automaton()
for idx, f in enumerate(ss):
A.add_word(f, (idx, f))
A.make_automaton()
print("Extract Feautre Vectors of train data")
fvec = create_fvec(s, A, n_jobs)
print("Learn classifier")
cls = RidgeClassifierCV(alphas=np.logspace(-3, 3, 10), normalize=True)
cls.fit(fvec, y)
print("Load test data")
y_test, s_test = load_data(test_file)
print("Extract Feature Vector of test data")
fvec_test = create_fvec(s_test, A, n_jobs)
print("Predict")
print(cls.score(fvec_test, y_test))
def main():
train_input = pd.read_csv('../input/train.csv')
test_input = pd.read_csv('../input/test.csv')
data = pd.concat([train_input, test_input])
# We don't have data on whether person is delinquint
featurizer = CreditScoreFeaturizer() # Create our own features
print "Transforming dataset into features..."
##Create matrix of features from raw dataset
X = featurizer.fit_transform(data)
X_train = X[:len(train_input)]
X_test = X[len(train_input):]
## Use any model that we might find appropriate
model = RidgeClassifierCV(alphas=[ 0.1, 1., 10. ])
##Create the object and set relevant parameters
#model = LogisticRegression(C=10) # Can also switch different models (e.g. Ridge)
##Set target variable y
y = train_input.SeriousDlqin2yrs
print "Cross validating..."
print np.mean(cross_val_score(model, X_train, y, scoring='roc_auc')) # Scoring metric is now AUC
print "Training final model..."
model = model.fit(X_train, y)
class TestServinator(unittest.TestCase):
def setUp(self):
data = load_iris()
x_train, x_test, y_train, y_test = train_test_split(data.data,
data.target,
test_size=0.2,
random_state=67,
stratify=data.target)
self.model = RidgeClassifierCV(normalize=True,
scoring='logloss',
cv=3,
class_weight='balanced')
self.model.fit(x_train, y_train)
self.test_data = x_test
self.test_target = y_test
model_backend = ModelBackend(self.model)
# self.app = servinator('test', model_backend).test_client()
def test_json_to_model_input(self):
raw_json = '''[{"end_date": "2014-10-08T14:52:44-04:00", "location": "Louisville, KY", "pledged": "70.0", "goal": "10000.0", "category": "Food", "author": "Joe Banet", "backers": "4", "blurb": "Krazy Joe's soon to be famous kimchi and bourbon barrels which have long ago been enjoyed come together at last. Bourbon aged kimchi!", "title": "Krazy Joe's Bourbon Barrel Kimchi", "full_text": "I like kimchi. I like to make kimchi. I think I'm pretty good at it. My goal is to create a Bourbon barrel aged kimchi and share it with the world. This is just a start to company that could greatly expand and diversify into many products that all have one common denominator...kimchi. Thank you for your interest and support! ; "}]'''
expected_data = {
'author': {0: 'joe banet'},
'backers': {0: '4'},
'blurb': {0: 'krazy joes soon to be famous kimchi and bourbon barrels which have long ago been enjoyed come together at last bourbon aged kimchi'},
'day': {0: 8},
'dayofweek': {0: 2},
'dayofyear': {0: 281},
'full_text': {0: 'i like kimchi i like to make kimchi i think im pretty good at it my goal is to create a bourbon barrel aged kimchi and share it with the world this is just a start to company that could greatly expand and diversify into many products that all have one common denominator kimchi thank you for your interest and support'},
'goal': {0: '10000.0'},
'hour': {0: 18},
'loc1': {0: ''},
'loc2': {0: 'louisville'},
'loc3': {0: 'ky'},
'minute': {0: 52},
'month': {0: 10},
'pledged': {0: '70.0'},
'title': {0: 'krazy joes bourbon barrel kimchi'},
'weekday': {0: 2},
'weekofyear': {0: 41},
'year': {0: 2014}}
df_json = _json_to_model_input(raw_json)
df_expected = pd.DataFrame(expected_data)
# Sort them for comparison
df_json = df_json.loc[:, sorted(df_json.columns.values)]
df_expected = df_expected.loc[:, sorted(df_expected.columns.values)]
self.assertTrue(df_expected.equals(df_json))
def test_e2e(self):
'''test full load and predict of training data and assert it matches
offline result'''
pass
def _fit_estimator(self, rocket, X, y):
transformed_x = rocket.fit_transform(X)
ridge = RidgeClassifierCV(alphas=np.logspace(-3, 3, 10), normalize=True)
ridge.fit(transformed_x, y)
return [
make_pipeline(rocket, ridge),
transformed_x if self.save_transformed_data else None,
]
def test_ridge_classifier_with_scoring(filter_, scoring, cv):
# non-regression test for #14672
# check that RidgeClassifierCV works with all sort of scoring and
# cross-validation
scoring_ = make_scorer(scoring) if callable(scoring) else scoring
clf = RidgeClassifierCV(scoring=scoring_, cv=cv)
# Smoke test to check that fit/predict does not raise error
clf.fit(filter_(X_iris), y_iris).predict(filter_(X_iris))
def do_rcv(X_test, X_train, Y_train):
# creating a classifier of loss function "hinge" and penalty function "l2"
clf = RidgeClassifierCV()
print "starts fitting"
print clf.fit(X_train, Y_train)
print "finished fitting, starts predictions"
Y_pred = clf.predict(X_test)
print "finished predictions"
return Y_pred
def ridge_classification(X_train, X_test, y_train, y_test):
X_train, X_test = preprocess(X_train, X_test)
from sklearn.linear_model import RidgeClassifierCV
classifier = RidgeClassifierCV()
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
y_pred = np.round(y_pred).flatten()
plot_model(classifier, X_train, y_train, y_test, y_pred,
"RidgeClassifierCV")
def __init__(self,
alphas=(0.1, 1.0, 10.0),
fit_intercept=True,
normalize=False,
scoring=None,
cv=None,
class_weight=None):
_RidgeClassifierCV.__init__(self, alphas, fit_intercept, normalize,
scoring, cv, class_weight)
BaseWrapperClf.__init__(self)
def _fit_estimator(self, rocket, X, y):
transformed_x = rocket.fit_transform(X)
scaler = StandardScaler(with_mean=False)
scaler.fit(transformed_x, y)
ridge = RidgeClassifierCV(alphas=np.logspace(-3, 3, 10))
ridge.fit(scaler.transform(transformed_x), y)
return [
make_pipeline(rocket, scaler, ridge),
transformed_x if self.save_transformed_data else None,
]
def ridge_classfier_cv_selected_feature():
raw_frame=thal_data()
x=raw_frame.drop(['sugar','age','cardiographic','angina','slope','thal','log_cholestoral'],axis=1)
y=raw_frame['thal']
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=5)
clf = RidgeClassifierCV(alphas=[1e-3, 1e-2, 1e-1, 1]).fit(x_train, y_train)
global train_score
train_score.append(clf.score(x_train,y_train))
global test_score
test_score.append(clf.score(x_test,y_test))
def ridge_classfier_cv_withlog():
raw_frame=thal_data()
x=raw_frame.drop(['thal','pressure','cholestoral','age','heart_rate'],axis=1)
y=raw_frame['thal']
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=5)
clf = RidgeClassifierCV(alphas=[1e-3, 1e-2, 1e-1, 1]).fit(x_train, y_train)
global train_score
train_score.append(clf.score(x_train,y_train))
global test_score
test_score.append(clf.score(x_test,y_test))
def ridge(X, X_train, X_val, y_train, y_val, X_test, y_test):
model = RidgeClassifierCV(alphas=[1e-3, 1e-2, 1e-1, 1])
model.fit(X_train, y_train)
sh = X.shape
save_model(model, sh)
tr_f1, val_f1, test_f1, tr_acc, val_acc, test_acc = model_performance(
model, X_train, y_train, X_val, y_val, X_test, y_test)
return tr_f1, val_f1, test_f1
def train(self, X_train, Y_train):
_ = generate_kernels(100, 10)
apply_kernels(np.zeros_like(X_train)[:, 1:], _)
input_length = X_train.shape[1]
self.kernels = generate_kernels(input_length, self.num_kernels)
X_transform = apply_kernels(X_train, self.kernels)
self.classifier = RidgeClassifierCV(alphas=10**np.linspace(-3, 3, 10),
normalize=True)
return self.classifier.fit(X_transform, Y_train)
def agent(path="./", dataset="", ratio=False, seg=0.75, folder="temp"):
current_process().name = dataset
start1 = time.time()
train_x, train_y = load_from_tsfile_to_dataframe(
f"{path}/{dataset}/{dataset}_TRAIN.ts")
test_x, test_y = load_from_tsfile_to_dataframe(
f"{path}/{dataset}/{dataset}_TEST.ts")
print(f"{dataset}: Train Shape {train_x.shape}")
print(f"{dataset}: Test Shape {test_x.shape}")
scaler = StandardScaler()
transform_time1 = time.time()
mod_train = PAAStat(paa_=ratio, seg_=seg).transform(train_x.values)
mod_train = scaler.fit(mod_train).transform(mod_train)
mod_test = PAAStat(paa_=ratio, seg_=seg).transform(test_x.values)
mod_test = scaler.transform(mod_test)
transform_time2 = time.time()
model = RidgeClassifierCV(alphas=np.logspace(-3, 3, 10), normalize=True)
train_time1 = time.time()
model.fit(mod_train, train_y)
preds = model.predict(mod_test)
train_time2 = time.time()
acc1 = accuracy_score(preds, test_y) * 100
end1 = time.time()
print(
f"Dataset: {dataset}, AccuracyRidge: {acc1}, Time taken: {(end1 - start1)/60}, "
f"Transfrom_time: {(transform_time2-transform_time1)/60}, train_time: {(train_time2-train_time1)/60}"
)
results = pd.DataFrame({
'Dataset':
dataset,
'AccuracyRidge': [acc1],
'Time (min)': [(end1 - start1) / 60],
'Transfrom_time (min)': [(transform_time2 - transform_time1) / 60],
'train_time (min)': [(train_time2 - train_time1) / 60]
})
temp_path = './' + folder
if not os.path.exists(temp_path):
os.mkdir(temp_path)
results.to_csv(os.path.join(temp_path + f'/{dataset}.csv'), index=False)
def ridge(y, pred, i, wt=1):
b = y[:, i]
b.ravel()
c = pred[:,i] > 0.4
c.ravel()
print('score before (class {})'.format(i), f1_score(b, c,))
clf = RidgeClassifierCV(alphas=[ 0.001, 0.01, 0.1, 1],class_weight={0:1, 1:wt}, normalize=True).fit(pred,b ) #
#clf = RidgeClassifierCV(alphas=[0.001, 0.01, 0.1, 1], class_weight='balanced', cv=10).fit(pred, b) #
ri_pred = clf.predict(pred)
print('score after (class {})'.format(i), f1_score(b, ri_pred))
#print('score after (class {})'.format(i),clf.score(pred, b))
f1_before.append(f1_score(b, c,))
f1_after.append(f1_score(b, ri_pred))
return clf
def train_model(X, Y):
print "Training LR..."
modelLR = LogisticRegression(penalty='l1', C=100, tol=1e-10)
modelLR.fit(X.toarray(), Y)
print "Training RC..."
modelRC = RidgeClassifierCV(alphas=[ 0.1, 1., 10. ])
modelRC.fit(X.toarray(), Y)
print "Training GBC..."
modelGBC = GradientBoostingClassifier(subsample=0.5, max_depth=6, n_estimators=50)
modelGBC.fit(X.toarray(), Y)
return modelGBC, modelRC, modelLR
def test_ridge_regression_custom_scoring(filter_, cv):
# check that custom scoring is working as expected
# check the tie breaking strategy (keep the first alpha tried)
def _dummy_score(y_test, y_pred):
return 0.42
alphas = np.logspace(-2, 2, num=5)
clf = RidgeClassifierCV(alphas=alphas,
scoring=make_scorer(_dummy_score),
cv=cv)
clf.fit(filter_(X_iris), y_iris)
assert clf.best_score_ == pytest.approx(0.42)
# In case of tie score, the first alphas will be kept
assert clf.alpha_ == pytest.approx(alphas[0])
def __init__(self,
num_features=10000,
max_dilations_per_kernel=32,
random_state=None,
alphas=np.logspace(-3, 3, 13),
normalize=True,
memory=None,
verbose=False,
scoring=None,
class_weight=None,
**kwargs):
"""
MiniRocketClassifier is recommended for up to 10k time series.
For a larger dataset, you can use MINIROCKET (in Pytorch).
scoring = None --> defaults to accuracy.
"""
self.steps = [('minirocketmultivariate',
MiniRocketMultivariate(
num_features=num_features,
max_dilations_per_kernel=max_dilations_per_kernel,
random_state=random_state)),
('ridgeclassifiercv',
RidgeClassifierCV(alphas=alphas,
normalize=normalize,
scoring=scoring,
class_weight=class_weight,
**kwargs))]
self.num_features, self.max_dilations_per_kernel, self.random_state = num_features, max_dilations_per_kernel, random_state
self.alphas, self.normalize, self.scoring, self.class_weight, self.kwargs = alphas, normalize, scoring, class_weight, kwargs
self.memory = memory
self.verbose = verbose
self._validate_steps()
def agent(path, dataset, seg, folder, paa=True):
start = time.time()
train_x, train_y = load_from_tsfile_to_dataframe(
f"{path}/{dataset}/{dataset}_TRAIN.ts")
test_x, test_y = load_from_tsfile_to_dataframe(
f"{path}/{dataset}/{dataset}_TEST.ts")
print(f"{dataset}: Train Shape {train_x.shape}")
print(f"{dataset}: Test Shape {test_x.shape}")
model = Pipeline([('data_transform', PAAStat(paa_=paa, seg_=seg)),
('model',
RidgeClassifierCV(alphas=np.logspace(-3, 3, 10),
normalize=True,
class_weight='balanced'))])
model.fit(train_x.values, train_y)
preds = model.predict(test_x.values)
acc1 = accuracy_score(preds, test_y) * 100
end = time.time()
results = pd.DataFrame({
'Dataset': dataset,
'AccuracyRidge': [acc1],
'Time': [end - start]
})
print(results)
temp_path = './' + folder
if not os.path.exists(temp_path):
os.mkdir(temp_path)
results.to_csv(os.path.join(temp_path + f'/{dataset}.csv'), index=False)
def fit(self, X, y):
"""Build a pipeline containing the ROCKET transformer and RidgeClassifierCV.
Parameters
----------
X : nested pandas DataFrame of shape [n_instances, 1]
Nested dataframe with univariate time-series in cells.
y : array-like, shape = [n_instances] The class labels.
Returns
-------
self : object
"""
X, y = check_X_y(X, y)
self.n_classes = np.unique(y).shape[0]
self.classes_ = class_distribution(np.asarray(y).reshape(-1, 1))[0][0]
for index, classVal in enumerate(self.classes_):
self.class_dictionary[classVal] = index
self.classifier = rocket_pipeline = make_pipeline(
Rocket(
num_kernels=self.num_kernels,
random_state=self.random_state,
n_jobs=self.n_jobs,
),
RidgeClassifierCV(alphas=np.logspace(-3, 3, 10), normalize=True),
)
rocket_pipeline.fit(X, y)
self._is_fitted = True
return self
def _fit(self, X, y):
"""Build a pipeline containing the ROCKET transformer and RidgeClassifierCV.
Parameters
----------
X : 3D np.array of shape = [n_instances, n_dimensions, series_length]
The training data.
y : array-like, shape = [n_instances]
The class labels.
Returns
-------
self :
Reference to self.
Notes
-----
Changes state by creating a fitted model that updates attributes
ending in "_" and sets is_fitted flag to True.
"""
self._pipeline = rocket_pipeline = make_pipeline(
Rocket(
num_kernels=self.num_kernels,
random_state=self.random_state,
n_jobs=self._threads_to_use,
),
RidgeClassifierCV(alphas=np.logspace(-3, 3, 10), normalize=True),
)
rocket_pipeline.fit(X, y)
return self
class RocketClassifier(sklearn.pipeline.Pipeline):
def __init__(self,
num_kernels=10_000,
normalize_input=True,
random_state=None,
alphas=np.logspace(-3, 3, 7),
normalize_features=True,
memory=None,
verbose=False,
scoring=None,
class_weight=None,
**kwargs):
"""
RocketClassifier is recommended for up to 10k time series.
For a larger dataset, you can use ROCKET (in Pytorch).
scoring = None --> defaults to accuracy.
Rocket args:
num_kernels : int, number of random convolutional kernels (default 10,000)
normalize_input : boolean, whether or not to normalise the input time series per instance (default True)
random_state : int (ignored unless int due to compatability with Numba), random seed (optional, default None)
"""
self.steps = [('rocket',
Rocket(num_kernels=num_kernels,
normalise=normalize_input,
random_state=random_state)),
('ridgeclassifiercv',
RidgeClassifierCV(alphas=alphas,
normalize=normalize_features,
scoring=scoring,
class_weight=class_weight,
**kwargs))]
store_attr()
self._validate_steps()
def init_classifiers(seed):
return {
'AdaBoostClassifier':
AdaBoostClassifier(random_state=seed),
'BaggingClassifier':
BaggingClassifier(random_state=seed),
'ExtraTreesClassifier':
ExtraTreesClassifier(random_state=seed),
'GradientBoostingClassifier':
GradientBoostingClassifier(random_state=seed),
'RandomForestClassifier':
RandomForestClassifier(random_state=seed),
'XGBClassifier':
xgb.XGBClassifier(),
'LogisticRegression':
LogisticRegression(random_state=seed),
'PassiveAggressiveClassifier':
PassiveAggressiveClassifier(random_state=seed),
'RidgeClassifier':
RidgeClassifier(random_state=seed),
'RidgeClassifierCV':
RidgeClassifierCV(),
'SGDClassifier':
SGDClassifier(random_state=seed),
#'KNeighborsClassifier': KNeighborsClassifier(),
#'RadiusNeighborsClassifier': RadiusNeighborsClassifier(),
'MLPClassifier':
MLPClassifier(random_state=seed),
'DecisionTreeClassifier':
DecisionTreeClassifier(random_state=seed),
'ExtraTreeClassifier':
ExtraTreeClassifier(random_state=seed)
}
def get_logistic_regression_coefs_l2(self,
category,
clf=RidgeClassifierCV()):
''' Computes l2-penalized logistic regression score.
Parameters
----------
category : str
category name to score
category : str
category name to score
Returns
-------
(coefficient array, accuracy, majority class baseline accuracy)
'''
try:
from sklearn.cross_validation import cross_val_predict
except:
from sklearn.model_selection import cross_val_predict
y = self._get_mask_from_category(category)
X = TfidfTransformer().fit_transform(self._X)
clf.fit(X, y)
y_hat = cross_val_predict(clf, X, y)
acc, baseline = self._get_accuracy_and_baseline_accuracy(y, y_hat)
return clf.coef_[0], acc, baseline
def classify_connectivity(X,
y,
sss,
classifier_name,
n_jobs=-1,
subjects=None):
""" Returns 100 shuffle split scores
"""
if classifier_name == 'logreg_l1':
classifier = LogisticRegression(penalty='l1',
dual=False,
random_state=42)
elif classifier_name == 'logreg_l2':
classifier = LogisticRegression(penalty='l2', random_state=42)
elif classifier_name == 'ridge':
classifier = RidgeClassifierCV(alphas=np.logspace(-3, 3, 7))
elif classifier_name == 'svc_l2':
classifier = LinearSVC(penalty='l2', random_state=42)
elif classifier_name == 'svc_l1':
classifier = LinearSVC(penalty='l1', dual=False, random_state=42)
p = Parallel(n_jobs=n_jobs, verbose=5)(
delayed(train_and_test)(classifier, X, y, train, test, subjects)
for train, test in sss)
return np.asarray(p)
class RocketClassifier(sklearn.pipeline.Pipeline):
"""Time series classification using ROCKET features and a linear classifier"""
def __init__(self, num_kernels=10_000, normalize_input=True, random_state=None,
alphas=np.logspace(-3, 3, 7), normalize_features=True, memory=None, verbose=False, scoring=None, class_weight=None, **kwargs):
"""
RocketClassifier is recommended for up to 10k time series.
For a larger dataset, you can use ROCKET (in Pytorch).
scoring = None --> defaults to accuracy.
Rocket args:
num_kernels : int, number of random convolutional kernels (default 10,000)
normalize_input : boolean, whether or not to normalise the input time series per instance (default True)
random_state : Optional random seed (default None)
"""
try:
import sktime
from sktime.transformations.panel.rocket import Rocket
except ImportError:
print("You need to install sktime to be able to use RocketClassifier")
self.steps = [('rocket', Rocket(num_kernels=num_kernels, normalise=normalize_input, random_state=random_state)),
('ridgeclassifiercv', RidgeClassifierCV(alphas=alphas, normalize=normalize_features, scoring=scoring,
class_weight=class_weight, **kwargs))]
store_attr()
self._validate_steps()
def _train_probas_for_estimator(self, y, idx):
rs = 255 if self.random_state == 0 else self.random_state
rs = (None if self.random_state is None else
(rs * 37 * (idx + 1)) % np.iinfo(np.int32).max)
rng = check_random_state(rs)
indices = range(self.n_instances_)
subsample = rng.choice(self.n_instances_, size=self.n_instances_)
oob = [n for n in indices if n not in subsample]
results = np.zeros((self.n_instances_, self.n_classes_))
if len(oob) == 0:
return results, 1, oob
clf = make_pipeline(
StandardScaler(with_mean=False),
RidgeClassifierCV(alphas=np.logspace(-3, 3, 10)),
)
clf.fit(self.transformed_data_[idx].iloc[subsample], y[subsample])
preds = clf.predict(self.transformed_data_[idx].iloc[oob])
weight = clf.steps[1][1].best_score_
for n, pred in enumerate(preds):
results[oob[n]][self._class_dictionary[pred]] += weight
return results, weight, oob
def generate_submission(models):
X = pd.concat([
pd.read_csv(inp)[full_labels] for inp in [
"../models/{}/train_meta_probs.csv".format(model)
for model in models
]
],
axis=1)
X_test = pd.concat([
pd.read_csv(inp)[full_labels] for inp in
["../models/{}/test_meta_probs.csv".format(model) for model in models]
],
axis=1)
col_names = [
"{}_{}".format(i, j)
for i in ["model_{}".format(k) for k in range(len(models))]
for j in full_labels
]
X.columns, X_test.columns = col_names, col_names
folds = get_folds()
print("===Ridge===")
ridge_cv = RidgeClassifierCV(alphas=[
0.01, 0.05, 0.1, 0.2, 0.5, 1, 2, 5, 10, 15, 20, 30, 40, 50, 70, 100
],
cv=folds).fit(X, y)
print("best alpha value is: {}".format(ridge_cv.alpha_))
ridge_model = RidgeClassifier(alpha=ridge_cv.alpha_).fit(X, y)
print(accuracy_score(y, ridge_model.predict(X)))
test_df['label'] = pd.Series(
ridge_model.predict(X_test)).map(full_num_label_mapping)
test_df['label'] = test_df['label'].map(lambda x: "unknown"
if x not in labels else x)
test_df.to_csv("ridge_on_{}_models.csv".format(len(models)), index=False)
def get_classifier(clf_name):
svmClf = SVC(gamma='auto')
advancedSvmClf = SVC(gamma='auto',
kernel='rbf',
probability=True,
class_weight='balanced',
C=120000000)
ridgeClf = RidgeClassifierCV(alphas=np.logspace(-3, 3, 10), normalize=True)
randomForestClf = RandomForestClassifier(n_estimators=200,
max_depth=7,
random_state=0)
gradientBoostClf = GradientBoostingClassifier(n_estimators=200,
max_depth=7,
random_state=0)
xgbClf = XGBClassifier(n_estimators=500, max_depth=5)
adaBoostClf = AdaBoostClassifier()
lgbmClf = LGBMClassifier(n_estimators=1000)
if clf_name == 'svm':
return svmClf
elif clf_name == 'advanced_svm':
return advancedSvmClf
elif clf_name == 'ridge':
return ridgeClf
elif clf_name == 'random_forest':
return randomForestClf
elif clf_name == 'gradient_boosting':
return gradientBoostClf
elif clf_name == 'xgb':
return xgbClf
elif clf_name == 'ada_boost':
return adaBoostClf
elif clf_name == 'lgbm':
return lgbmClf
def fit(self, X, y):
trainx1, trainx2 = zip(*X)
self.count_vect = CountVectorizer(analyzer='word', ngram_range=(1, 2))
self.count_vect.fit(list(trainx1)+list(trainx2))
X_train_counts1 = self.count_vect.transform(trainx1)
X_train_counts2 = self.count_vect.transform(trainx2)
X_train_counts = np.concatenate((X_train_counts1.toarray(),X_train_counts2.toarray()),axis=1)
self.clf = RidgeClassifierCV().fit(X_train_counts, y)
return self
def main():
start_time = time.time()
train_feats, train_labels, test_feats = get_data(TRAIN_FILE, TEST_FILE)
# try RidgeClassifier with manual cross validation (k-fold)
lr = RidgeClassifier().fit(train_feats, train_labels)
cv_preds = cross_validation.cross_val_predict(lr, train_feats, train_labels, cv=10)
print("cross validation accuracy:", metrics.accuracy_score(train_labels, cv_preds))
# try automatic RidgeClassifierCV (k-fold)
lrcv = RidgeClassifierCV(cv=10).fit(train_feats, train_labels)
print("built in ridge cv accuracy:", lrcv.score(train_feats, train_labels))
# use cross validated model to predict test labels
preds = lrcv.predict(test_feats).astype(str)
for i in range(preds.shape[0]):
if preds[i] == '1': preds[i] = 'TRUE'
else: preds[i] = 'FALSE'
np.savetxt("attractiveness_predictions.csv", preds, fmt="%s", newline="\n")
print("time taken:", time.time()-start_time, "seconds")
def main():
train_input = pd.read_csv('train.csv')
test_input = pd.read_csv('test.csv')
data = pd.concat([train_input, test_input])
featurizer = CreditScoreFeaturizer()
print "Transforming dataset into features..."
##Create matrix of features from raw dataset
X = featurizer.fit_transform(data)
X_train = X[:len(train_input)]
X_test = X[len(train_input):]
## Use any model that we might find appropriate
model = RidgeClassifierCV(alphas=[ 0.1, 1., 10. ])
##Create the object and set relevant parameters
#model = LogisticRegression(C=10)
##Set target variable y
y = train_input.SeriousDlqin2yrs
print "Cross validating..."
print np.mean(cross_val_score(model, X_train, y, scoring='roc_auc', cv=10))
print "Training final model..."
model = model.fit(X_train, y)
n_models=5
bag_size=0.70
models = [LogisticRegression(C=10) for _ in xrange(n_models)]
model = Bagging(models, bag_size)
#Fit Final Model
model.fit(X_train, y)
print "Create predictions on submission set..."
create_submission(model, X_test, test_input)
def setUp(self):
data = load_iris()
x_train, x_test, y_train, y_test = train_test_split(data.data,
data.target,
test_size=0.2,
random_state=67,
stratify=data.target)
self.model = RidgeClassifierCV(normalize=True,
scoring='logloss',
cv=3,
class_weight='balanced')
self.model.fit(x_train, y_train)
self.test_data = x_test
self.test_target = y_test
model_backend = ModelBackend(self.model)
class GClassifier(BaseEstimator, ClassifierMixin):
def __init__(self):
pass
def fit(self, X, y):
trainx1, trainx2 = zip(*X)
self.count_vect = CountVectorizer(analyzer='word', ngram_range=(1, 2))
self.count_vect.fit(list(trainx1)+list(trainx2))
X_train_counts1 = self.count_vect.transform(trainx1)
X_train_counts2 = self.count_vect.transform(trainx2)
X_train_counts = np.concatenate((X_train_counts1.toarray(),X_train_counts2.toarray()),axis=1)
self.clf = RidgeClassifierCV().fit(X_train_counts, y)
return self
def predict(self, X):
testx1, testx2 = zip(*X)
X_test_counts1 = self.count_vect.transform(testx1)
X_test_counts2 = self.count_vect.transform(testx2)
X_test_counts = np.concatenate((X_test_counts1.toarray(),X_test_counts2.toarray()),axis=1)
return self.clf.predict(X_test_counts)
def sklearn_ridge_cv(Xtrain,Ytrain,Xtest,Ytest,*args,**kwargs):
clf = RidgeClassifierCV(fit_intercept=True)
clf.fit(Xtrain,Ytrain)
return clf.score(Xtest,Ytest)
# Nicely print the confusion matrix
print " " * 4,
for label in labels:
print " %s" % label,
print
for i, label1 in enumerate(labels):
print label1,
for j, label2 in enumerate(labels):
print "%4d" % cm[i, j],
print
from sklearn.linear_model import RidgeClassifierCV
clf = RidgeClassifierCV().fit(X_train, y_train)
print("Accuracy = ", clf.score(X_test, y_test)),
print_cm(confusion_matrix(y_test,
clf.predict(X_test),
labels=populations), populations)
# Plot coefficients
coef = np.mean(np.abs(clf.coef_), axis=0)
f, ax = plt.subplots(figsize=(10, 4))
plt.bar(left=range(coef.size), height=coef)
# ppl.bar(ax, left=range(coef.size), height=coef, xticklabels=None,
# annotate=False)
plt.savefig("ridge.png")
plt.tight_layout()
plt.savefig(fname, format="png", bbox_extra_artists=(lgd,), bbox_inches="tight")
plt.close()
if __name__ == "__main__":
# generate some fake data, split, and scale
X, y = make_classification(n_samples=1000, n_informative=5, n_redundant=6, random_state=4)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=4)
scaler = StandardScaler().fit(X_train)
X_train_standard = scaler.transform(X_train)
X_test_standard = scaler.transform(X_test)
# specify classifiers
ridge = RidgeClassifierCV(alphas=np.logspace(-3, 1, 20))
lasso = LogisticRegressionCV(Cs=np.logspace(-3, 1, num=20))
forest = RandomForestClassifier(n_estimators=100, n_jobs=-1)
# train the classifiers
ridge.fit(X_train_standard, y_train)
lasso.fit(X_train_standard, y_train)
forest.fit(X_train, y_train)
# predicted values
ridge_preds = ridge.predict(X_test_standard)
lasso_preds = lasso.predict(X_test_standard)
forest_preds = forest.predict(X_test)
# confusion matrices
c1 = confusion_matrix(y_test, ridge_preds)
from sklearn.neighbors import RadiusNeighborsRegressor
from sklearn.ensemble import AdaBoostRegressor
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import RidgeClassifierCV
from sklearn.ensemble import GradientBoostingClassifier
gdc = GradientBoostingClassifier()
lr = LogisticRegression()
clf = svm.SVR()
et = ExtraTreesClassifier()
rgr = RadiusNeighborsRegressor()
forest = RandomForestRegressor(n_estimators = 100, n_jobs = 2, oob_score=True)
adaboost = AdaBoostRegressor()
nb = GaussianNB()
rd = RidgeClassifierCV()
kf = KFold(report.shape[0], n_folds = 5)
for train_index, test_index in kf:
#print("TRAIN:", train_index, "TEST:", test_index)
X_train, X_test = variables.ix[list(train_index),], variables.ix[list(test_index),]
y_train = report['survey_participant'].ix[list(train_index),]
y_test = report['survey_participant'].ix[list(test_index),]
forest.fit(X_train,y_train)
adaboost.fit(X_train,y_train)
gdc.fit(X_train, y_train)
rd.fit(X_train, y_train)
rgr.fit(X_train, y_train)
nb.fit(X_train, y_train)
lr.fit(X_train, y_train)
et.fit(X_train, y_train)