def test_ridge_gcv_sample_weights(
gcv_mode, X_constructor, fit_intercept, n_features, y_shape, noise):
alphas = [1e-3, .1, 1., 10., 1e3]
rng = np.random.RandomState(0)
n_targets = y_shape[-1] if len(y_shape) == 2 else 1
X, y = _make_sparse_offset_regression(
n_samples=11, n_features=n_features, n_targets=n_targets,
random_state=0, shuffle=False, noise=noise)
y = y.reshape(y_shape)
sample_weight = 3 * rng.randn(len(X))
sample_weight = (sample_weight - sample_weight.min() + 1).astype(int)
indices = np.repeat(np.arange(X.shape[0]), sample_weight)
sample_weight = sample_weight.astype(float)
X_tiled, y_tiled = X[indices], y[indices]
cv = GroupKFold(n_splits=X.shape[0])
splits = cv.split(X_tiled, y_tiled, groups=indices)
kfold = RidgeCV(
alphas=alphas, cv=splits, scoring='neg_mean_squared_error',
fit_intercept=fit_intercept)
# ignore warning from GridSearchCV: DeprecationWarning: The default of the
# `iid` parameter will change from True to False in version 0.22 and will
# be removed in 0.24
with ignore_warnings(category=DeprecationWarning):
kfold.fit(X_tiled, y_tiled)
ridge_reg = Ridge(alpha=kfold.alpha_, fit_intercept=fit_intercept)
splits = cv.split(X_tiled, y_tiled, groups=indices)
predictions = cross_val_predict(ridge_reg, X_tiled, y_tiled, cv=splits)
kfold_errors = (y_tiled - predictions)**2
kfold_errors = [
np.sum(kfold_errors[indices == i], axis=0) for
i in np.arange(X.shape[0])]
kfold_errors = np.asarray(kfold_errors)
X_gcv = X_constructor(X)
gcv_ridge = RidgeCV(
alphas=alphas, store_cv_values=True,
gcv_mode=gcv_mode, fit_intercept=fit_intercept)
gcv_ridge.fit(X_gcv, y, sample_weight=sample_weight)
if len(y_shape) == 2:
gcv_errors = gcv_ridge.cv_values_[:, :, alphas.index(kfold.alpha_)]
else:
gcv_errors = gcv_ridge.cv_values_[:, alphas.index(kfold.alpha_)]
assert kfold.alpha_ == pytest.approx(gcv_ridge.alpha_)
assert_allclose(gcv_errors, kfold_errors, rtol=1e-3)
assert_allclose(gcv_ridge.coef_, kfold.coef_, rtol=1e-3)
assert_allclose(gcv_ridge.intercept_, kfold.intercept_, rtol=1e-3)
def _get_mse_profiling(self, x, y, alphas=None):
"""Calculate prediction RMSE.
Use GroupKFold where a group is a combination of input size and number
of workers. The prediction of a group is done when it is out of the
training set.
"""
# Training set is 2/3 of the data
groups = self._groups.loc[x.index]
cv = GroupKFold(n_splits=3)
preds = None
for train_ix, test_ix in cv.split(x, groups=groups):
# train_ix and test_ix starts from 0, so we use iloc
x_train, y_train = x.iloc[train_ix], y.iloc[train_ix]
x_test = x.iloc[test_ix]
# Choose best alpha value for regularization based on training set
lm = self._choose_alpha(x_train, y_train, alphas)
lm.fit(x_train, y_train)
pred = pd.DataFrame(lm.predict(x_test), index=test_ix)
preds = pred if preds is None else preds.append(
pred, verify_integrity=True)
return self._calc_mse(y, preds.sort_index())
def fit_predict(self,x_train,y_train, x_predict):
"""Use local regression to predict values for unknown data.
Arguments:
x_train = The training data spectra.
y_train = The values of the quantity being predicted for the training data
x_predict = The unknown spectra for which y needs to be predicted.
"""
self.neighbors.fit(x_train)
predictions = []
coeffs = []
intercepts = []
for i in range(x_predict.shape[0]):
print('Predicting spectrum ' + str(i + 1))
x_temp = np.array(x_predict[i])
foo, ind = self.neighbors.kneighbors([x_temp])
x_train_local = np.squeeze(x_train[ind])
y_train_local = np.squeeze(y_train[ind])
cv = GroupKFold(n_splits=3)
cv = cv.split(x_train_local, y_train_local,
groups=y_train_local)
self.model.fit(x_train_local, y_train_local)
predictions.append(self.model.predict([x_temp])[0])
coeffs.append(self.model.coef_)
intercepts.append(self.model.intercept_)
return predictions, coeffs, intercepts
def _split(self, x, y):
cv = GroupKFold(n_splits=3)
groups = self._groups.loc[x.index]
for train_ix, test_ix in cv.split(x, groups=groups):
# train_ix and test_ix starts from 0, so we use iloc
x_train = x.iloc[train_ix]
y_train = y.iloc[train_ix]
x_test = x.iloc[test_ix]
yield x_train, y_train, x_test, test_ix
def plot_group_kfold():
from sklearn.model_selection import GroupKFold
groups = [0, 0, 0, 1, 1, 1, 1, 2, 2, 3, 3, 3]
plt.figure(figsize=(10, 2))
plt.title("GroupKFold")
axes = plt.gca()
axes.set_frame_on(False)
n_folds = 12
n_samples = 12
n_iter = 3
n_samples_per_fold = 1
cv = GroupKFold(n_splits=3)
mask = np.zeros((n_iter, n_samples))
for i, (train, test) in enumerate(cv.split(range(12), groups=groups)):
mask[i, train] = 1
mask[i, test] = 2
for i in range(n_folds):
# test is grey
colors = ["grey" if x == 2 else "white" for x in mask[:, i]]
# not selected has no hatch
boxes = axes.barh(bottom=range(n_iter), width=[1 - 0.1] * n_iter,
left=i * n_samples_per_fold, height=.6, color=colors,
hatch="//", edgecolor="k", align='edge')
for j in np.where(mask[:, i] == 0)[0]:
boxes[j].set_hatch("")
axes.barh(bottom=[n_iter] * n_folds, width=[1 - 0.1] * n_folds,
left=np.arange(n_folds) * n_samples_per_fold, height=.6,
color="w", edgecolor='k', align="edge")
for i in range(12):
axes.text((i + .5) * n_samples_per_fold, 3.5, "%d" %
groups[i], horizontalalignment="center")
axes.invert_yaxis()
axes.set_xlim(0, n_samples + 1)
axes.set_ylabel("CV iterations")
axes.set_xlabel("Data points")
axes.set_xticks(np.arange(n_samples) + .5)
axes.set_xticklabels(np.arange(1, n_samples + 1))
axes.set_yticks(np.arange(n_iter + 1) + .3)
axes.set_yticklabels(
["Split %d" % x for x in range(1, n_iter + 1)] + ["Group"])
plt.legend([boxes[0], boxes[1]], ["Training set", "Test set"], loc=(1, .3))
plt.tight_layout()
def test_knn_rbf_groupkfold():
nan_roc_auc_scorer = make_scorer(nan_roc_auc_score)
rng = np.random.RandomState(123)
iris = load_iris()
X = iris.data
# knn = KNeighborsClassifier(n_neighbors=4)
forest = RandomForestClassifier(n_estimators=100, random_state=123)
bool_01 = [True if item == 0 else False for item in iris['target']]
bool_02 = [True if (item == 1 or item == 2) else False for item in
iris['target']]
groups = []
y_new = []
for ind, _ in enumerate(bool_01):
if bool_01[ind]:
groups.append('attribute_A')
y_new.append(0)
if bool_02[ind]:
throw = rng.rand()
if throw < 0.5:
groups.append('attribute_B')
else:
groups.append('attribute_C')
throw2 = rng.rand()
if throw2 < 0.5:
y_new.append(0)
else:
y_new.append(1)
y_new_bool = [True if item is 1 else False for item in y_new]
cv_obj = GroupKFold(n_splits=3)
cv_obj_list = list(cv_obj.split(X, np.array(y_new_bool), groups))
sfs1 = SFS(forest,
k_features=3,
forward=True,
floating=False,
cv=cv_obj_list,
scoring=nan_roc_auc_scorer,
verbose=0
)
sfs1 = sfs1.fit(X, y_new)
expect = {
1: {'cv_scores': np.array([0.52, nan, 0.72]), 'avg_score': 0.62,
'feature_idx': (1,)},
2: {'cv_scores': np.array([0.42, nan, 0.65]), 'avg_score': 0.53,
'feature_idx': (1, 2)},
3: {'cv_scores': np.array([0.47, nan, 0.63]),
'avg_score': 0.55,
'feature_idx': (1, 2, 3)}}
dict_compare_utility(d_actual=sfs1.subsets_, d_desired=expect, decimal=1)
NUM_FOLDS = 4
EPOCHS = 30
BATCH_SIZE = 256
BAGS = 16
kf = GroupKFold(n_splits=NUM_FOLDS)
shape = None
for bag in range(BAGS):
fold = 0
val_loss = np.ones((EPOCHS, NUM_FOLDS), np.float32)
for train, val in kf.split(x_train, y_train, G):
model.set_weights(weights)
model.reset_states()
tensorboard = TensorBoard(
log_dir='./logs/gru_fold_{}_bag_{}'.format(fold, bag))
history = model.fit(x_train[train],
y_train[train],
batch_size=BATCH_SIZE,
validation_data=(x_train[val], y_train[val]),
epochs=EPOCHS,
shuffle=True,
verbose=1,
callbacks=[tensorboard])
val_loss[:, fold] = history.history['val_loss']
fold += 1
# In[14]:
ch_arr = [0, 1, 2]
# In[15]:
path = '6ts_4space_imgs_time_aug_resnet50_models'
os.makedirs(path)
# In[21]:
models_w_arr = []
fold = 0
for train_index, val_index in group_kfold.split(train_df,
train_df['wind_speed'],
train_df['storm_id']):
print(fold)
fold += 1
os.makedirs(path + '/fold_%d' % (fold - 1))
image_datasets = {
'train':
WindDataset(train_imgs, train_df['wind_speed'].values,
train_df['storm_id'].values, 'train', train_index,
data_transforms['train']),
'val':
WindDataset(train_imgs, train_df['wind_speed'].values,
train_df['storm_id'].values, 'val', val_index,
data_transforms['val'])
}
# In[11]:
oof = np.zeros(df_train.shape[0])
sub = np.zeros(df_test.shape[0])
feat_imp_df = pd.DataFrame({'feat': feats, 'imp': 0.0})
gkf = GroupKFold(n_splits=5)
# In[12]:
print('train shape {} test shape {}'.format(df_train.shape, df_test.shape))
# In[13]:
for i, (trn_idx, val_idx) in enumerate(
gkf.split(df_train,
groups=(df_train.date.map(str) + '_' +
df_train.link_id.map(str)))):
print(
'------------------------------{} fold------------------------------'.
format(i))
X_trn, Y_trn, W_trn = df_train.iloc[trn_idx][feats], df_train.iloc[
trn_idx].label, df_train.iloc[trn_idx].weight
X_val, Y_val, W_val = df_train.iloc[val_idx][feats], df_train.iloc[
val_idx].label, df_train.iloc[val_idx].weight
X_sub = df_test[feats]
clf = LGBMRegressor(
num_leaves=63,
learning_rate=0.02,
n_estimators=100000,
subsample=0.6,
embedding_numeric = Dense(512, activation='relu')(input_numeric)
inputs.append(input_numeric)
embeddings.append(embedding_numeric)
x = Concatenate()(embeddings)
x = Dense(256, activation='relu')(x)
x = Dense(128, activation='relu')(x)
x = Dropout(0.5)(x)
output = Dense(199, activation='softmax')(x)
model = Model(inputs, output)
return model
n_splits = 5
kf = GroupKFold(n_splits=n_splits)
score = []
for i_, (tdx, vdx) in enumerate(kf.split(X, y, X['GameId'])):
print(f'Fold : {i_+1}')
X_train, X_val, y_train, y_val = X.iloc[tdx], X.iloc[vdx], y[tdx], y[vdx]
X_train = [np.absolute(X_train[i]) for i in cat
] + [X_train[num]] # + [X_train[env1]] + [X_train[env2]]
X_val = [np.absolute(X_val[i])
for i in cat] + [X_val[num]] # + [X_val[env1]] + [X_val[env2]]
model = model_NN()
model.compile(optimizer='Adam',
loss='categorical_crossentropy',
metrics=[])
es = EarlyStopping(monitor='val_CRPS',
mode='min',
restore_best_weights=True,
feature_importance = {}
num_dic = {}
trans_x = np.transpose(X)
max_val = 0
for i in range(num_vars):
feature_importance[names[i]] = ms_array[i]
num_dic[i] = ms_array[i]
if max_val < num_dic[i]:
max_val = num_dic[i]
best_feature = i
for a in range(10):
x1 = X[:, best_feature].reshape(-1, 1)
group_kfold = GroupKFold(n_splits=10)
group_kfold.get_n_splits(x1, y, groups)
acc_arr = []
for train_index, test_index in group_kfold.split(X, y, groups):
X_train = []
X_test = []
y_train = []
y_test = []
for id in train_index:
X_train.append(x1[id])
for id in test_index:
X_test.append(x1[id])
for id in train_index:
y_train.append(y[id])
for id in test_index:
y_test.append(y[id])
clf = RandomForestClassifier().fit(X_train, y_train)
tmp_score = clf.score(X_test, y_test)
acc_arr.append(tmp_score)
def cv():
data_x, data_y, body_ids = build_data()
holdout_ids = set([int(x.rstrip()) for x in file('hold_out_ids.txt')])
print 'len(holdout_ids): ', len(holdout_ids)
holdout_idx = [t for (t, x) in enumerate(body_ids) if x in holdout_ids]
test_x = data_x[holdout_idx] # features of test set
print 'holdout_x.shape: '
print test_x.shape
test_y = data_y[holdout_idx]
print Counter(test_y)
#return 1
# to obtain test dataframe for model averaging
body = pd.read_csv("train_bodies.csv")
stances = pd.read_csv("train_stances.csv")
data = pd.merge(stances, body, how='left', on='Body ID')
targets = ['agree', 'disagree', 'discuss', 'unrelated']
targets_dict = dict(zip(targets, range(len(targets))))
data['target'] = map(lambda x: targets_dict[x], data['Stance'])
test_df = data.ix[holdout_idx]
cv_ids = set([int(x.rstrip()) for x in file('training_ids.txt')])
print 'len(cv_ids): ', len(cv_ids)
cv_idx = [t for (t, x) in enumerate(body_ids) if x in cv_ids]
cv_x = data_x[cv_idx]
print 'cv_x.shape: '
print cv_x.shape
cv_y = data_y[cv_idx]
groups = body_ids[cv_idx] # GroupKFold will make sure all samples
# having the same "Body ID" will appear in the same fold
w = np.array([1 if y == 3 else 4 for y in cv_y])
print 'w:'
print w
print np.mean(w)
scores = []
wscores = []
pscores = []
n_folds = 10
best_iters = [0] * n_folds
kf = GroupKFold(n_splits=n_folds)
# need to create disjoint sets for training and validation
for fold, (trainInd, validInd) in enumerate(kf.split(cv_x, cv_y, groups)):
continue
print 'fold %s' % fold
x_train = cv_x[trainInd]
y_train = cv_y[trainInd]
x_valid = cv_x[validInd]
y_valid = cv_y[validInd]
idx_valid = np.array(cv_idx)[validInd]
print 'perfect_score: ', perfect_score(y_valid)
print Counter(y_valid)
#break
dtrain = xgb.DMatrix(x_train, label=y_train, weight=w[trainInd])
dvalid = xgb.DMatrix(x_valid, label=y_valid, weight=w[validInd])
watchlist = [(dtrain, 'train'), (dvalid, 'eval')]
bst = xgb.train(
params_xgb,
dtrain,
num_round,
watchlist,
verbose_eval=10,
#feval = eval_metric,
#maximize = True,
early_stopping_rounds=80)
#pred_y = bst.predict(dvalid, ntree_limit=bst.best_ntree_limit)
pred_y = bst.predict(dvalid, ntree_limit=bst.best_ntree_limit).reshape(
y_valid.shape[0], 4)
print 'predicted probabilities: '
print pred_y
pred_y = np.argmax(pred_y, axis=1)
print 'predicted label indices: '
print pred_y
print 'best iterations: ', bst.best_ntree_limit
best_iters[fold] = bst.best_ntree_limit
#pred_y = bst.predict(dvalid)
print pred_y
#print Counter(pred_y)
#pred_y = np.argmax(bst.predict(dvalid, ntree_limit=bst.best_ntree_limit), axis=1)
print 'pred_y.shape'
print pred_y.shape
print 'y_valid.shape'
print y_valid.shape
#s = fscore(pred_y, y_valid)
#s_perf = perfect_score(y_valid)
predicted = [LABELS[int(a)] for a in pred_y]
actual = [LABELS[int(a)] for a in y_valid]
# print out the headline & body text for incorrect predictions
#show_incorrect_pred(actual, predicted, idx_valid)
s, _ = score_submission(actual, predicted)
s_perf, _ = score_submission(actual, actual)
wscore = float(s) / s_perf
print 'fold %s, score = %f, perfect_score %f, weighted percentage %f' % (
fold, s, s_perf, wscore)
scores.append(s)
pscores.append(s_perf)
wscores.append(wscore)
#break
print 'scores:'
print scores
print 'mean score:'
print np.mean(scores)
print 'perfect scores:'
print pscores
print 'mean perfect score:'
print np.mean(pscores)
print 'w scores:'
print wscores
print 'mean w score:'
print np.mean(wscores)
print 'best iters:'
print best_iters
print 'mean best_iter:'
m_best = np.mean(best_iters)
print m_best
#m_best = best_iters[0]
m_best = 500
#m_best = 500
#m_best = 600
#return 1
# use the same parameters to train with full cv data, test on hold-out data
print 'test on holdout set'
dtrain = xgb.DMatrix(cv_x, label=cv_y, weight=w)
dtest = xgb.DMatrix(test_x, label=test_y)
watchlist = [(dtrain, 'train')]
clf = xgb.train(
params_xgb,
dtrain,
#num_round,
int(m_best),
watchlist,
feval=eval_metric,
verbose_eval=10)
pred_prob_holdout_y = clf.predict(dtest).reshape(test_y.shape[0],
4) # probabilities
pred_holdout_y = np.argmax(pred_prob_holdout_y, axis=1)
print 'pred_holdout_y.shape:'
print pred_holdout_y.shape
print 'test_y.shape:'
print test_y.shape
#s_test = fscore(pred_holdout_y, test_y)
#s_test_perf = perfect_score(test_y)
predicted = [LABELS[int(a)] for a in pred_holdout_y]
actual = [LABELS[int(a)] for a in test_y]
report_score(actual, predicted)
print Counter(predicted)
test_df['actual'] = actual
test_df['predicted'] = predicted
test_df['prob_0'] = pred_prob_holdout_y[:, 0]
test_df['prob_1'] = pred_prob_holdout_y[:, 1]
test_df['prob_2'] = pred_prob_holdout_y[:, 2]
test_df['prob_3'] = pred_prob_holdout_y[:, 3]
#test_df[['Headline','Body ID', 'Stance', 'actual', 'predicted']].to_csv('predtest.csv', index=False)
test_df[[
'Headline', 'Body ID', 'Stance', 'actual', 'predicted', 'prob_0',
'prob_1', 'prob_2', 'prob_3'
]].to_csv('predtest_cor2.csv', index=False)
def train_classifier_GS(self, x,y, groups):
if self.classifier == "SVM":
clf = svm.SVC(kernel='rbf', probability=True)
if self.OneVsRest:
param_grid = {
'estimator__C': [1, 10, 100, 1000],
'estimator__gamma': [0.001, 0.0001]
}
else:
param_grid = {
'C': [1, 10, 100, 1000],
'gamma': [0.001, 0.0001]
}
elif self.classifier == "RF":
clf = RandomForestClassifier()
if self.OneVsRest:
param_grid = {
'estimator__n_estimators': [100, 250, 500, 750, 1000],
'estimator__max_features': ['auto', 'log2'],
'estimator__max_depth': [4, 6, 8],
'estimator__criterion': ['gini', 'entropy']
}
else:
param_grid = {
'n_estimators': [100, 250, 500, 750, 1000],
'max_features': ['auto', 'log2'],
'max_depth': [4, 6, 8],
'criterion': ['gini', 'entropy']
}
elif self.classifier == "XGB":
clf = xgb.XGBClassifier()
if self.OneVsRest:
param_grid = {
'estimator__max_depth': [4, 6, 8],
'estimator__learning_rate': [0.01, 0.1, 0.3]
}
else:
param_grid = {
'max_depth': [4, 6, 8],
'learning_rate': [0.01, 0.1, 0.3]
}
#Apply PCA transformation to data
if self.n > 0:
self.pca.fit_transform(x)
x_transformed =self.pca.transform(x)
else:
x_transformed = x
# OneVsRest Classifier
if self.OneVsRest:
clf = OneVsRestClassifier(clf)
# Define cross validation method
#kfolds = StratifiedKFold(n_splits=5, shuffle=True, random_state=50)
group_kfold = GroupKFold(n_splits=5)
kfolds = group_kfold.split(x_transformed, y, groups)
#Get scoring metrics
scoring = self.get_scoring()
#Perform Grid Search
grid = GridSearchCV(clf, param_grid=param_grid, cv=kfolds, n_jobs=1,
scoring=scoring, refit='f1_weighted', verbose=1, return_train_score=True)
grid.fit(x_transformed, y)
#Print and log results
self.save_results(grid)
self.save_best_result(grid)
#Save best estimator
self.clf = grid.best_estimator_
#disp = plot_precision_recall_curve(best_clf, x_transformed, y)
return
def main(args):
# Load the parameters from json file
params_dir = args.params_dir
json_path = os.path.join(params_dir, 'params.json')
assert os.path.isfile(
json_path), "No json configuration file found at {}".format(json_path)
params = utils.Params(json_path)
# use GPU if available
params.cuda = torch.cuda.is_available()
# Set the random seed for reproducible experiments
torch.manual_seed(params.seed)
if params.cuda:
torch.cuda.manual_seed(params.seed)
# Set the logger
model_dir = args.output_dir
if not os.path.exists(model_dir):
os.makedirs(model_dir)
utils.set_logger(os.path.join(model_dir, 'train.log'))
logging.info("************ Validation fold: {} ************".format(
args.fold))
# Create the input data pipeline
logging.info("Loading the datasets...")
config_dict = {
'image_dir': os.path.join(args.input_dir, 'train'),
'csv_path': os.path.join(args.input_dir, 'train.csv')
}
train_data = DataPreprocess(config_dict)
df, target_cols, num_targets = train_data.df, train_data.target_cols, train_data.num_targets
# check for debug mode
if args.debug:
params.num_epochs = 1
df = df.sample(n=100, random_state=params.seed).reset_index(drop=True)
# update params
params.mode = args.mode
params.num_targets = num_targets
params.target_cols = target_cols
# split data into folds and pass to the model
Fold = GroupKFold(n_splits=params.num_folds)
groups = df['PatientID'].values
for n, (train_index, valid_index) in enumerate(
Fold.split(df, df[params.target_cols], groups)):
df.loc[valid_index, 'fold'] = int(n)
df['fold'] = df['fold'].astype(int)
# get training and validation data using folds
train_df = df[df.fold != args.fold].reset_index(drop=True)
valid_df = df[df.fold == args.fold].reset_index(drop=True)
# get dataloaders
train_dataloader = dataloader.fetch_dataloader(train_df,
params,
data='train')
valid_dataloader = dataloader.fetch_dataloader(valid_df,
params,
data='valid')
logging.info("- done.")
# Define the model and optimizer
model = RANZCRModel(params, pretrained=True).model
if params.cuda:
model = model.to(torch.device('cuda'))
optimizer = optim.Adam(model.parameters(),
lr=params.learning_rate,
amsgrad=False)
scheduler = lr_scheduler.ReduceLROnPlateau(optimizer,
mode='min',
factor=0.1,
patience=2,
verbose=True)
# fetch loss function and metrics
loss_fn = nn.BCEWithLogitsLoss()
metrics = models.metrics
# Train the model
logging.info("Starting training for {} epoch(s)".format(params.num_epochs))
train_and_evaluate(model, train_dataloader, valid_dataloader,
valid_df[params.target_cols].values, optimizer,
scheduler, loss_fn, metrics, params, model_dir)
logger.debug(f"Number of rows in train: {n_train}")
logger.debug(f"Number of rows in test: {n_test}")
logger.debug(f"Using features:{train_use.columns.values}")
categorical_cols = ["district", "layout", "direction", "structure"]
####################
## Train model
####################
folds = GroupKFold(n_splits=5)
oof = np.zeros(len(train_use))
predictions = np.zeros(len(test_use))
feature_importance_df = pd.DataFrame()
for fold, (train_idx,
val_idx) in enumerate(folds.split(train_use, groups=train_group)):
print(f"Fold {fold+1}")
train_data = lgb.Dataset(train_use.iloc[train_idx],
label=target_log[train_idx],
categorical_feature=categorical_cols)
val_data = lgb.Dataset(train_use.iloc[val_idx],
label=target_log[val_idx],
categorical_feature=categorical_cols)
num_round = N_ROUNDS
callbacks = [log_evaluation(logger, period=100)]
clf = lgb.train(params,
train_data,
num_round,
valid_sets=[train_data, val_data],
verbose_eval=False,
early_stopping_rounds=100,
print(f'Processing fold {fold}')
model = get_model()
model.to(DEVICE)
train_idx, valid_idx = fold_info[fold]
f'Proportions valid / train: {len(valid_idx) / len(train_idx)}'
train_dl, valid_dl = generate_train_valid_dls(ds, train_idx, valid_idx)
optimizer, scheduler = create_optimizer_scheduler(model, train_dl, EPOCHS)
train_losses, valid_losses, accumulated_lrs, accumulated_dice_metrics = train(fold, EPOCHS, train_dl, valid_dl, optimizer, scheduler, patience = PATIENCE)
return train_losses, valid_losses, accumulated_lrs, accumulated_dice_metrics
# In[50]:
fold_info = [(train_idx, valid_idx) for fold, (train_idx, valid_idx) in tqdm(enumerate(group_kfold.split(ds.slices,
groups = groups)), total=FOLDS)]
# In[51]:
# from fastai.data.core import DataLoaders
# train_idx, valid_idx = fold_info[0]
# train_ds, valid_ds = create_subset(ds, train_idx, valid_idx)
# dls = DataLoaders.from_dsets(train_ds, valid_ds, bs=BATCH_SIZE, num_workers=2)
# assert(dls.bs == BATCH_SIZE)
# In[52]:
# Weight train samples by the inverse of how frequently the installation_id appears
train_features["sample_weight"] = 1 / train_features.groupby(
"installation_id")["accuracy_group"].transform("count")
# Estimate accuracy_group proportions in the test set by using sample weights in train instead of sampling
accuracy_groups = train_features.groupby(
"accuracy_group")["sample_weight"].agg("sum")
accuracy_group_proportions = list(accuracy_groups / accuracy_groups.sum())
assessment_encoder = LabelEncoder()
train_features["assessment"] = assessment_encoder.fit_transform(
train_features["assessment"])
group_kfold = GroupKFold(n_splits=5)
models = []
qwk_scores = []
for train_index, val_index in group_kfold.split(
train_features, groups=train_features["installation_id"]):
model, qwk_score = train_and_evaluate(
train_features,
list(train_index),
list(val_index),
accuracy_group_proportions,
)
models.append(model)
qwk_scores.append(qwk_score)
print(f"QWK score: {np.mean(qwk_scores)}")
# Predict on test set
X_test, installation_ids = (
test_features.drop(["installation_id"], axis=1),
test_features["installation_id"],
param_dist = {
'n_estimators': stats.randint(100, 1000),
'learning_rate': stats.uniform(0.01, 0.1),
'subsample': stats.uniform(0.3, 0.7),
'max_depth': [3, 4, 5, 6, 7, 8, 9],
'colsample_bytree': stats.uniform(0.5, 0.45),
'min_child_weight': [1, 2, 3]
}
n_splits = 50
groups = data_train['Scenario']
group_kfold = GroupKFold(n_splits=n_splits)
rmse = list()
for number, (train_idx, test_idx) in enumerate(
group_kfold.split(data_train, groups=groups)):
print(f'Fold {number + 1} of {n_splits}')
X_train, Y_train = x_.iloc[train_idx, :], y_.iloc[train_idx]
X_test, Y_test = x_.iloc[test_idx, :], y_.iloc[test_idx]
groups_train = groups[train_idx]
group_kfold_inner = GroupKFold(n_splits=3)
xgb_regressor_model = xgb.XGBRegressor(objective='reg:squarederror')
xgb_regressor_search = RandomizedSearchCV(
xgb_regressor_model,
param_distributions=param_dist,
n_iter=10,
scoring='neg_mean_squared_error',
cv=group_kfold_inner.split(X_train, groups=groups_train),
refit=1,
data = data.merge(user_ais_data, on=['user_id', 'aisle_id'], how='left')
del user_ais_data
train_set = data[data['eval_set'] == 'train']
del data
"""
任务1： 用一个单决策树来进行拟合，对参数进行调整。
"""
from sklearn.model_selection import GroupKFold
kf = GroupKFold(n_splits=5)
train_indexes = []
test_indexes = []
for i, (train_index, test_index) in enumerate(
kf.split(train_set, groups=train_set['user_id'].values)):
train_indexes.append(train_index)
test_indexes.append(test_index)
train_index = train_indexes[0]
test_index = test_indexes[0]
training = train_set.iloc[train_index, :]
testing = train_set.iloc[test_index, :]
del train_set
"""
Decision Tree
"""
col = list(training.columns)
col.remove('reordered')
group_kfold = GroupKFold(n_splits=NFOLD)
sub_train['g'] = sub_train.index % NFOLD
CAT = list(set(X.columns) & set(utils_cat.ALL))
# =============================================================================
# cv
# =============================================================================
dtrain = lgb.Dataset(X, y, categorical_feature=CAT)
gc.collect()
ret = lgb.cv(param,
dtrain,
9999,
folds=group_kfold.split(X, sub_train['y'], sub_train['g']),
early_stopping_rounds=100,
verbose_eval=50,
seed=SEED)
result = f"CV auc-mean: {ret['auc-mean'][-1]}"
print(result)
utils.send_line(result)
# =============================================================================
# imp
# =============================================================================
dtrain = lgb.Dataset(X, y, categorical_feature=CAT)
model = lgb.train(param, dtrain, len(ret['auc-mean']))
imp = ex.getImp(model).sort_values(['gain', 'feature'],
def multiclass_one_vs_rest(x,
y,
model_type='svm',
plot=False,
verbose=False,
run_cv=False):
# First split the data into training and test sets
x_train, x_test, y_train, y_test = train_test_split(
x, y, test_size=0.33, random_state=RANDOM_SEED)
# pick the base classifier based on the model_type paramemter
if model_type is 'logistic':
base_model = LogisticRegression(random_state=RANDOM_SEED,
class_weight='balanced')
elif model_type is 'tree':
base_model = DecisionTreeClassifier(random_state=RANDOM_SEED,
class_weight='balanced')
elif model_type is 'adaboost':
base_model = AdaBoostClassifier(random_state=RANDOM_SEED)
elif model_type is 'forest':
# base case
base_model = RandomForestClassifier(random_state=RANDOM_SEED,
class_weight='balanced')
# this gives no improvement over base case
#base_model = RandomForestClassifier(random_state=RANDOM_SEED, class_weight='balanced', n_estimators = 50, max_depth=20)
# no improvement here either
#base_model = RandomForestClassifier(random_state=RANDOM_SEED, class_weight='balanced', n_estimators = 100, max_depth=40)
elif model_type is 'nnet':
base_model = MLPClassifier(random_state=RANDOM_SEED)
elif model_type is 'extra':
base_model = ExtraTreesClassifier(random_state=RANDOM_SEED)
else:
#base case
base_model = SVC(kernel='linear',
random_state=RANDOM_SEED,
class_weight='balanced',
probability=True)
# no improvement here
#base_model = SVC(kernel='linear', random_state=RANDOM_SEED, class_weight='balanced', activation="logistic", max_iter=500)
# create the OvR model using the base classifier
# model = OneVsRestClassifier(base_model, n_jobs=10)
# create OvR model with base classifier and feature selection
model = CustomBRClassifier(base_model)
# train the model using the training data
fit_start = time.time()
model.fit(x_train, y_train)
fit_end = time.time()
if verbose:
print('------ model info ----------')
print('one vs all ' + model_type + ' is a multi-label classifier: ' +
str(model.multilabel_))
print('one vs all ' + model_type + ' number of classes: ' +
str(model.classes_))
print('one vs all ' + model_type + ' elapsed training time: ' +
str(fit_end - fit_start))
# check the accuracy on the training data
if verbose:
print('------ training data ----------')
fpr_train, tpr_train, auc_train = check_predictions(
model, (model_type + " - train"), x_train, y_train, plot, verbose)
# check the accuracy on the test data
if verbose:
print('------ test data ----------')
fpr_test, tpr_test, auc_test = check_predictions(model,
(model_type + " - test"),
x_test, y_test, plot,
verbose)
# generate_roc_hist(y_test, model.predict_proba(x_test))
# get the cross-validation score
if run_cv:
accuracy_scorer = make_scorer(calculate_overall_accuracy)
kf = GroupKFold(5)
cv_accuracy_scores = []
cv_auc_scores = []
for train_index, test_index in kf.split(x,
y=y,
groups=np.arange(x.shape[0])):
X_train, X_test = x.iloc[train_index,
np.arange(0, x.shape[1])], x.iloc[
test_index,
np.arange(0, x.shape[1])]
Y_train, Y_test = y[train_index], y[test_index]
model.fit(X_train, Y_train)
cv_auc_scores.append(
roc_auc_score(Y_test,
model.predict_proba(X_test),
average='micro'))
cv_accuracy_scores.append(
calculate_overall_accuracy(Y_test,
model.predict_proba(X_test)))
cv_accuracy_scores = np.mean(np.array(cv_accuracy_scores))
cv_auc_scores = np.mean(np.array(cv_auc_scores))
if verbose:
print('------ CV scores ----------')
print('one vs all ' + model_type + ' CV accuracy scores ' +
str(cv_accuracy_scores))
print('one vs all ' + model_type + ' CV AUC scores ' +
str(cv_auc_scores))
return fpr_train, tpr_train, auc_train, fpr_test, tpr_test, auc_test
logger.debug(f"Number of rows in train: {n_train}")
logger.debug(f"Number of rows in test: {n_test}")
logger.debug(f"Using features:{train_use.columns.values}")
categorical_cols = ["district", "layout", "direction", "structure"]
####################
## Train model
####################
folds = GroupKFold(n_splits=5)
oof = np.zeros(len(train_use))
predictions = np.zeros(len(test_use))
feature_importance_df = pd.DataFrame()
for fold, (train_idx,
val_idx) in enumerate(folds.split(train_use,
groups=group_for_kfold)):
print(f"Fold {fold+1}")
train_data = lgb.Dataset(train_use.iloc[train_idx],
label=target_log[train_idx],
categorical_feature=categorical_cols)
val_data = lgb.Dataset(train_use.iloc[val_idx],
label=target_log[val_idx],
categorical_feature=categorical_cols)
num_round = N_ROUNDS
callbacks = [log_evaluation(logger, period=100)]
clf = lgb.train(params,
train_data,
num_round,
valid_sets=[train_data, val_data],
verbose_eval=False,
early_stopping_rounds=100,
def RandomForest(X, Y, groups, n_trees):
lpgo = GroupKFold(n_splits=14)
MAE = []
ECM = []
MAPE = []
R2_SCORE = []
relevant_features = []
N = np.size(Y[0])
for train_index, test_index in lpgo.split(X, Y, groups):
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = Y[train_index], Y[test_index]
#Normalización de los datos
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)
# Ajustar el modelo a la regresión simple
regressor = RandomForestRegressor(n_estimators=n_trees, random_state=0)
regressor.fit(X_train, y_train)
relevant_features.append(regressor.feature_importances_)
# Predecir los resultados de prueba
y_pred = regressor.predict(X_test)
ECM.append(mean_squared_error(y_test, y_pred,
multioutput='raw_values'))
MAE.append(
mean_absolute_error(y_test, y_pred, multioutput='raw_values'))
R2_SCORE.append(r2_score(y_test, y_pred, multioutput='raw_values'))
m = []
m.append(
np.mean(np.abs(
(y_test[:, 0] - y_pred[:, 0]) / y_test[:, 0])) * 100)
m.append(
np.mean(np.abs(
(y_test[:, 1] - y_pred[:, 1]) / y_test[:, 1])) * 100)
MAPE.append(m)
ECM_matrix = np.asmatrix(ECM)
MAE_matrix = np.asmatrix(MAE)
MAPE_matrix = np.asmatrix(MAPE)
R2_matrix = np.asmatrix(R2_SCORE)
for i in range(0, N):
print("El error cuadratrico medio de validación para la salida", i,
"es (ECM):", np.around(np.mean(ECM_matrix[:, i]),
decimals=3), "+-",
np.around(np.std(ECM_matrix[:, i]), decimals=3))
print("El error medio absoluto de validación para la salida", i,
"es (MAE):", np.around(np.mean(MAE_matrix[:, i]),
decimals=3), "+-",
np.around(np.std(MAE_matrix[:, i]), decimals=3))
print(
"El porcentaje de error medio absoluto de validación para la salida",
(i + 1), "es (MAPE):",
np.around(np.mean(MAPE_matrix[:, i]), decimals=3), "%", "+-",
np.around(np.std(MAPE_matrix[:, i]), decimals=3), "%")
print("Coeficiente de determinación para la salida", (i + 1),
"es (R2):", np.around(np.mean(R2_matrix[:, i])), "%", "+-",
np.around(np.std(R2_matrix[:, i]), decimals=3))
relevant_features = np.asmatrix(relevant_features)
print(np.mean(relevant_features, axis=0))
def __init__(self,
seed,
val_split=0.2,
shuffle=True,
cell_features=['expression'],
drug_features=['descriptors'],
use_landmark_genes=False,
use_combo_score=False,
feature_subsample=None,
scaling='std',
scramble=False,
cv_partition='overlapping',
cv=0):
"""Initialize data merging drug response, drug descriptors and cell line essay.
Shuffle and split training and validation set
Parameters
----------
seed: integer
seed for random generation
val_split : float, optional (default 0.2)
fraction of data to use in validation
cell_features: list of strings from 'expression', 'expression_5platform', 'mirna', 'proteome', 'all', 'categorical' (default ['expression'])
use one or more cell line feature sets: gene expression, microRNA, proteome
use 'all' for ['expression', 'mirna', 'proteome']
use 'categorical' for one-hot encoded cell lines
drug_features: list of strings from 'descriptors', 'latent', 'all', 'categorical', 'noise' (default ['descriptors'])
use dragon7 descriptors, latent representations from Aspuru-Guzik's SMILES autoencoder
trained on NSC drugs, or both; use random features if set to noise
use 'categorical' for one-hot encoded drugs
shuffle : True or False, optional (default True)
if True shuffles the merged data before splitting training and validation sets
scramble: True or False, optional (default False)
if True randomly shuffle dose response data as a control
feature_subsample: None or integer (default None)
number of feature columns to use from cellline expressions and drug descriptors
use_landmark_genes: True or False
only use LINCS1000 landmark genes
use_combo_score: bool (default False)
use combination score in place of percent growth (stored in 'GROWTH' column)
scaling: None, 'std', 'minmax' or 'maxabs' (default 'std')
type of feature scaling: 'maxabs' to [-1,1], 'maxabs' to [-1, 1], 'std' for standard normalization
"""
self.cv_partition = cv_partition
np.random.seed(seed)
df = NCI60.load_combo_response(use_combo_score=use_combo_score,
fraction=True)
logger.info('Loaded {} unique (CL, D1, D2) response sets.'.format(
df.shape[0]))
if 'all' in cell_features:
self.cell_features = ['expression', 'mirna', 'proteome']
else:
self.cell_features = cell_features
if 'all' in drug_features:
self.drug_features = ['descriptors', 'latent']
else:
self.drug_features = drug_features
for fea in self.cell_features:
if fea == 'expression' or fea == 'rnaseq':
self.df_cell_expr = NCI60.load_cell_expression_rnaseq(
ncols=feature_subsample,
scaling=scaling,
use_landmark_genes=use_landmark_genes)
df = df.merge(self.df_cell_expr[['CELLNAME']], on='CELLNAME')
elif fea == 'expression_u133p2':
self.df_cell_expr = NCI60.load_cell_expression_u133p2(
ncols=feature_subsample,
scaling=scaling,
use_landmark_genes=use_landmark_genes)
df = df.merge(self.df_cell_expr[['CELLNAME']], on='CELLNAME')
elif fea == 'expression_5platform':
self.df_cell_expr = NCI60.load_cell_expression_5platform(
ncols=feature_subsample,
scaling=scaling,
use_landmark_genes=use_landmark_genes)
df = df.merge(self.df_cell_expr[['CELLNAME']], on='CELLNAME')
elif fea == 'mirna':
self.df_cell_mirna = NCI60.load_cell_mirna(
ncols=feature_subsample, scaling=scaling)
df = df.merge(self.df_cell_mirna[['CELLNAME']], on='CELLNAME')
elif fea == 'proteome':
self.df_cell_prot = NCI60.load_cell_proteome(
ncols=feature_subsample, scaling=scaling)
df = df.merge(self.df_cell_prot[['CELLNAME']], on='CELLNAME')
elif fea == 'categorical':
df_cell_ids = df[['CELLNAME']].drop_duplicates()
cell_ids = df_cell_ids['CELLNAME'].map(
lambda x: x.replace(':', '.'))
df_cell_cat = pd.get_dummies(cell_ids)
df_cell_cat.index = df_cell_ids['CELLNAME']
self.df_cell_cat = df_cell_cat.reset_index()
for fea in self.drug_features:
if fea == 'descriptors':
self.df_drug_desc = NCI60.load_drug_descriptors(
ncols=feature_subsample, scaling=scaling)
df = df[df['NSC1'].isin(self.df_drug_desc['NSC'])
& df['NSC2'].isin(self.df_drug_desc['NSC'])]
elif fea == 'latent':
self.df_drug_auen = NCI60.load_drug_autoencoded_AG(
ncols=feature_subsample, scaling=scaling)
df = df[df['NSC1'].isin(self.df_drug_auen['NSC'])
& df['NSC2'].isin(self.df_drug_auen['NSC'])]
elif fea == 'categorical':
df_drug_ids = df[['NSC1']].drop_duplicates()
df_drug_ids.columns = ['NSC']
drug_ids = df_drug_ids['NSC']
df_drug_cat = pd.get_dummies(drug_ids)
df_drug_cat.index = df_drug_ids['NSC']
self.df_drug_cat = df_drug_cat.reset_index()
elif fea == 'noise':
ids1 = df[['NSC1'
]].drop_duplicates().rename(columns={'NSC1': 'NSC'})
ids2 = df[['NSC2'
]].drop_duplicates().rename(columns={'NSC2': 'NSC'})
df_drug_ids = pd.concat([ids1, ids2]).drop_duplicates()
noise = np.random.normal(size=(df_drug_ids.shape[0], 500))
df_rand = pd.DataFrame(
noise,
index=df_drug_ids['NSC'],
columns=['RAND-{:03d}'.format(x) for x in range(500)])
self.df_drug_rand = df_rand.reset_index()
logger.info(
'Filtered down to {} rows with matching information.'.format(
df.shape[0]))
ids1 = df[['NSC1']].drop_duplicates().rename(columns={'NSC1': 'NSC'})
ids2 = df[['NSC2']].drop_duplicates().rename(columns={'NSC2': 'NSC'})
df_drug_ids = pd.concat([ids1, ids2
]).drop_duplicates().reset_index(drop=True)
n_drugs = df_drug_ids.shape[0]
n_val_drugs = int(n_drugs * val_split)
n_train_drugs = n_drugs - n_val_drugs
logger.info('Unique cell lines: {}'.format(df['CELLNAME'].nunique()))
logger.info('Unique drugs: {}'.format(n_drugs))
# df.to_csv('filtered.growth.min.tsv', sep='\t', index=False, float_format='%.4g')
# df.to_csv('filtered.score.max.tsv', sep='\t', index=False, float_format='%.4g')
if shuffle:
df = df.sample(frac=1.0, random_state=seed).reset_index(drop=True)
df_drug_ids = df_drug_ids.sample(
frac=1.0, random_state=seed).reset_index(drop=True)
self.df_response = df
self.df_drug_ids = df_drug_ids
self.train_drug_ids = df_drug_ids['NSC'][:n_train_drugs]
self.val_drug_ids = df_drug_ids['NSC'][-n_val_drugs:]
if scramble:
growth = df[['GROWTH']]
random_growth = growth.iloc[np.random.permutation(
np.arange(growth.shape[0]))].reset_index()
self.df_response[['GROWTH']] = random_growth['GROWTH']
logger.warn('Randomly shuffled dose response growth values.')
logger.info('Distribution of dose response:')
logger.info(self.df_response[['GROWTH']].describe())
self.total = df.shape[0]
self.n_val = int(self.total * val_split)
self.n_train = self.total - self.n_val
logger.info('Rows in train: {}, val: {}'.format(
self.n_train, self.n_val))
self.cell_df_dict = {
'expression': 'df_cell_expr',
'expression_5platform': 'df_cell_expr',
'expression_u133p2': 'df_cell_expr',
'rnaseq': 'df_cell_expr',
'mirna': 'df_cell_mirna',
'proteome': 'df_cell_prot',
'categorical': 'df_cell_cat'
}
self.drug_df_dict = {
'descriptors': 'df_drug_desc',
'latent': 'df_drug_auen',
'categorical': 'df_drug_cat',
'noise': 'df_drug_rand'
}
self.input_features = collections.OrderedDict()
self.feature_shapes = {}
for fea in self.cell_features:
feature_type = 'cell.' + fea
feature_name = 'cell.' + fea
df_cell = getattr(self, self.cell_df_dict[fea])
self.input_features[feature_name] = feature_type
self.feature_shapes[feature_type] = (df_cell.shape[1] - 1, )
for drug in ['drug1', 'drug2']:
for fea in self.drug_features:
feature_type = 'drug.' + fea
feature_name = drug + '.' + fea
df_drug = getattr(self, self.drug_df_dict[fea])
self.input_features[feature_name] = feature_type
self.feature_shapes[feature_type] = (df_drug.shape[1] - 1, )
logger.info('Input features shapes:')
for k, v in self.input_features.items():
logger.info(' {}: {}'.format(k, self.feature_shapes[v]))
self.input_dim = sum([
np.prod(self.feature_shapes[x])
for x in self.input_features.values()
])
logger.info('Total input dimensions: {}'.format(self.input_dim))
if cv > 1:
if cv_partition == 'disjoint':
pass
elif cv_partition == 'disjoint_cells':
y = self.df_response['GROWTH'].values
groups = self.df_response['CELLNAME'].values
gkf = GroupKFold(n_splits=cv)
splits = gkf.split(y, groups=groups)
self.cv_train_indexes = []
self.cv_val_indexes = []
for index, (train_index, val_index) in enumerate(splits):
print(index, train_index)
self.cv_train_indexes.append(train_index)
self.cv_val_indexes.append(val_index)
else:
y = self.df_response['GROWTH'].values
# kf = KFold(n_splits=cv)
# splits = kf.split(y)
skf = StratifiedKFold(n_splits=cv, random_state=seed)
splits = skf.split(y, discretize(y, bins=cv))
self.cv_train_indexes = []
self.cv_val_indexes = []
for index, (train_index, val_index) in enumerate(splits):
print(index, train_index)
self.cv_train_indexes.append(train_index)
self.cv_val_indexes.append(val_index)
def split_data(df,
ycol='0',
classify=False,
cv=5,
bins=0,
cutoffs=None,
groupcols=None,
ignore_categoricals=False,
verbose=True):
if groupcols is not None:
groups = make_group_from_columns(df, groupcols)
cat_cols = df.select_dtypes(['object']).columns
if ignore_categoricals:
df[cat_cols] = 0
else:
df[cat_cols] = df[cat_cols].apply(
lambda x: x.astype('category').cat.codes)
if ycol.isdigit():
ycol = df.columns[int(ycol)]
y = df.loc[:, ycol].as_matrix()
x = df.drop(ycol, axis=1).as_matrix()
features = df.drop(ycol, axis=1).columns.tolist()
if verbose:
print('Target column: {}'.format(ycol))
print(' count = {}, uniq = {}, mean = {:.3g}, std = {:.3g}'.format(
len(y), len(np.unique(y)), np.mean(y), np.std(y)))
print(
' min = {:.3g}, q1 = {:.3g}, median = {:.3g}, q3 = {:.3g}, max = {:.3g}'
.format(np.min(y), np.percentile(y, 25), np.median(y),
np.percentile(y, 75), np.max(y)))
if not classify:
y_even = discretize(y, bins=5, verbose=False)
elif bins >= 2:
y = discretize(y, bins=bins, min_count=cv, verbose=verbose)
elif cutoffs:
y = discretize(y, cutoffs=cutoffs, min_count=cv, verbose=verbose)
elif df[ycol].dtype in [np.dtype('float64'), np.dtype('float32')]:
warnings.warn(
'Warning: classification target is float; consider using --bins or --cutoffs'
)
y = y.astype(int)
if classify:
mask = np.ones(len(y), dtype=bool)
unique, counts = np.unique(y, return_counts=True)
for v, c in zip(unique, counts):
if c < cv:
mask[y == v] = False
x = x[mask]
y = y[mask]
removed = len(mask) - np.sum(mask)
if removed and verbose:
print('Removed {} rows in small classes: count < {}'.format(
removed, cv))
if groupcols is None:
if classify:
y_even = y
skf = StratifiedKFold(n_splits=cv, shuffle=True)
splits = skf.split(x, y_even)
else:
if classify:
groups = groups[mask]
gkf = GroupKFold(n_splits=cv)
splits = gkf.split(x, y, groups)
if verbose:
print()
return x, y, list(splits), features
col = list(train.columns)
col.remove('reordered')
col.remove('eval_set')
col.remove('user_id')
col.remove('product_id')
col.remove('order_id')
col.remove('department_id')
col.remove('aisle_id')
from sklearn.model_selection import GroupKFold
kf = GroupKFold(n_splits=5)
train_indexes = []
test_indexes = []
for i, (train_index, test_index) in enumerate(
kf.split(train, groups=train['user_id'].values)):
train_indexes.append(train_index)
test_indexes.append(test_index)
train_index = train_indexes[0]
test_index = test_indexes[0]
training = train.iloc[train_index, :]
testing = train.iloc[test_index, :]
del train
from sklearn.metrics import roc_auc_score
from sklearn.metrics import log_loss
del train_index, train_indexes, test_index, test_indexes, i
del test
import lightgbm as lgb
# Plot and save bar chart
plt.rcParams['xtick.labelsize'] = 8
ax = scaled_var_imp_df_sorted.plot.bar(y='scaled_importance',
x='variable',
rot=90,
figsize=(16, 12))
plt.tight_layout()
plt.savefig('FS_result_h2o.pdf', format='pdf', dpi=1200)
# Perform k-fold cv
# split on train - test dataset by group - according to no_folds
gkf = GroupKFold(n_splits=no_folds)
cv_fold = 0
for train_index, test_index in gkf.split(X, y, groups=groups):
cv_fold += 1
print("CV fold: ", cv_fold)
# print("Train Index: ", train_index)
# print("Test Index: ", test_index, "\n")
#print('Groups: ', groups,'\n')
trainX_data = X.loc[train_index]
trainy_data = y.loc[train_index]
testX_data = X.loc[test_index]
testy_data = y.loc[test_index]
# Save original 10cv folds with all features
train_set = pd.concat([trainX_data, trainy_data, groups],
axis=1,
def group_test_2(pre_x, kmeans_labels, names, num_dic, groups, num_vars,
meta_i):
# print('meta-i='+str(meta_i))
chosen_vars = np.zeros(meta_i)
chosen_values = np.zeros(meta_i)
# print('===')
for i in range(num_vars): #clean this routine up? check for errors?
old_val = 0
new_val = num_dic[i]
clust = kmeans_labels[i]
old_val = chosen_values[clust]
if old_val < new_val:
# print(names[i],old_val,new_val)
chosen_vars[clust] = int(i)
chosen_values[clust] = new_val
# print(chosen_vars)
# print(type(chosen_vars))
chosen_works = []
chosen_names = []
for qq in list(chosen_vars):
chosen_names.append(names[int(qq)])
chosen_works.append(int(qq))
X = pre_x[:, chosen_works]
# print(chosen_names)
# x_train,x_test,y_train,y_test=train_test_split(new_x,y,test_size=.3)
group_kfold = GroupKFold(n_splits=3)
group_kfold.get_n_splits(X, y, groups)
acc_arr = []
for train_index, test_index in group_kfold.split(X, y, groups):
X_train = []
X_test = []
y_train = []
y_test = []
# print(train_index)
# print(test_index)
for id in train_index:
X_train.append(X[id])
for id in test_index:
X_test.append(X[id])
for id in train_index:
y_train.append(y[id])
for id in test_index:
y_test.append(y[id])
# if model=='svm':
# clf=SVC().fit(X_train,y_train)
# elif model=='rf_extra':
# clf=ExtraTreesClassifier().fit(X_train,y_train)
# elif model=='rf':
# clf=RandomForestClassifier().fit(X_train,y_train)
# elif model=='nb':
# clf=GaussianNB().fit(X_train,y_train)
# elif model=='lr':
# clf=LogisticRegression().fit(X_train,y_train)
clf = RandomForestClassifier().fit(X_train, y_train)
# score=np.mean(cross_val_score(svm,X,y,cv=10))
# print(groupdic[groupid],modeldic[modelid],np.shape(X_test),np.shape(X_train))
tmp_score = clf.score(X_test, y_test)
acc_arr.append(tmp_score)
# print('accuracy='+','+str(qwer)+'\n')
return np.mean(acc_arr), chosen_names
if cross_test:
this_scores = cross_val_score(pipe, Sample, target, cv=10)
print('10次交叉验证：\n')
print('10次交叉验证的精确度', this_scores.view())
print('10次交叉验证的精确度平均值', this_scores.mean())
print('10次交叉验证的精确度方差', this_scores.std())
print('-----------------------------------------------')
group = sio.loadmat(
'D:\\1-embed\\4-Serial_GUI\\分类模型训练\\tmp\\group.mat')
group = group['group_individual']
group = group.reshape(-1)
print(group)
# gkf.split(X, y, groups=group)
gkf = GroupKFold(n_splits=11)
for train, test in gkf.split(Sample, target, groups=group):
print("train-%s test-%s" % (group[train], group[test]))
this_scores = cross_val_score(pipe,
Sample,
target,
groups=group,
cv=gkf)
print('5随机划分训练集：\n')
print('随机交叉验证的精确度', this_scores.view())
print('随机交叉验证的精确度平均值', this_scores.mean())
print('随机交叉验证的精确度方差', this_scores.std())
if save:
#使用dump()将数据序列化到文件中
fw = open('D:\\1-embed\\4-Serial_GUI\\分类模型训练\\tmp\\ModelFile.txt',
'wb')
def validate_train(model,
X,
y,
groups,
oversample=True,
n_splits=5,
dump=DUMP_DEFAULT,
model_folder=MODEL_FOLDER_DEFAULT,
metric=f1_score,
verbose=False,
num_importance=20):
kf = GroupKFold(n_splits=n_splits)
all_y = []
all_predicted = []
for train_index, test_index in kf.split(X, y, groups):
X_train, y_train = X[train_index], y[train_index]
X_test, y_test = X[test_index], y[test_index]
if oversample:
X_tmp, y_tmp = oversample_data(X_train, y_train, verbose=verbose)
model.train_model(X_tmp, y_tmp)
else:
model.train_model(X_train, y_train)
pred = model.predict_batch(X_test)
all_predicted.extend(pred)
all_y.extend(y_test)
print(">>> MODEL: ", model.model_name)
print("Params:", model.get_description())
all_y = model.encode2idx(all_y)
if metric is f1_score:
result = metric(all_y, all_predicted, average=None)
else:
result = metric(all_y, all_predicted)
if dump:
if oversample:
X_tmp, y_tmp = oversample_data(X, y, verbose=verbose)
model.train_model(X_tmp, y_tmp)
else:
model.train_model(X, y)
print("FEATURE_IMPORTANCE")
importances = model.get_feature_importance()
labels = model.get_labels()
print("=== labels {} ===".format(labels))
if importances is not None:
for imp_line, label in zip(importances, labels):
print("\nLABEL: ", label)
print("*" * 20)
print("\n --- TOP {} most important --- \n".format(
num_importance))
for n, val in imp_line[:num_importance]:
print("{}\t{}".format(n, np.round(val, 3)))
print("\n --- TOP {} anti features --- \n".format(
num_importance))
for n, val in imp_line[::-1][:num_importance]:
print("{}\t{}".format(n, np.round(val, 3)))
model.dump_model(os.path.join(model_folder, model.model_name))
print("== MODEL DUMPED ==")
print("classif_report:\n", classification_report(all_y, all_predicted))
log_results(all_y, all_predicted, model.model_name or model.model_name,
model)
return result
def run_cv_model_by_batch(train, test, splits, batch_col, feats, sample_submission, nn_epochs, nn_batch_size):
seed_everything(SEED)
K.clear_session()
config = tf.compat.v1.ConfigProto(intra_op_parallelism_threads=1,inter_op_parallelism_threads=1)
sess = tf.compat.v1.Session(graph=tf.compat.v1.get_default_graph(), config=config)
tf.compat.v1.keras.backend.set_session(sess)
oof_ = np.zeros((len(train), 11)) # build out of folds matrix with 11 columns, they represent our target variables classes (from 0 to 10)
preds_ = np.zeros((len(test), 11))
target = ['open_channels']
group = train['group']
kf = GroupKFold(n_splits=5)
splits = [x for x in kf.split(train, train[target], group)]
oof_pd = pd.DataFrame()
oof_pd['open_channels'] = train['open_channels']
new_splits = []
for sp in splits:
new_split = []
new_split.append(np.unique(group[sp[0]]))
new_split.append(np.unique(group[sp[1]]))
new_split.append(sp[1])
new_splits.append(new_split)
# pivot target columns to transform the net to a multiclass classification estructure (you can also leave it in 1 vector with sparsecategoricalcrossentropy loss function)
tr = pd.concat([pd.get_dummies(train.open_channels), train[['group']]], axis=1)
tr.columns = ['target_'+str(i) for i in range(11)] + ['group']
target_cols = ['target_'+str(i) for i in range(11)]
train_tr = np.array(list(tr.groupby('group').apply(lambda x: x[target_cols].values))).astype(np.float32)
train = np.array(list(train.groupby('group').apply(lambda x: x[feats].values)))
test = np.array(list(test.groupby('group').apply(lambda x: x[feats].values)))
for n_fold, (tr_idx, val_idx, val_orig_idx) in enumerate(new_splits[0:], start=0):
train_x, train_y = train[tr_idx], train_tr[tr_idx]
valid_x, valid_y = train[val_idx], train_tr[val_idx]
print(f'Our training dataset shape is {train_x.shape}')
print(f'Our validation dataset shape is {valid_x.shape}')
train_x_new = train
gc.collect()
shape_ = (None, train_x.shape[2]) # input is going to be the number of feature we are using (dimension 2 of 0, 1, 2)
model = Classifier(shape_)
# using our lr_schedule function
cb_lr_schedule = LearningRateScheduler(lr_schedule)
# Use Early-Stopping
callback_early_stopping = EarlyStopping(monitor='val_loss', patience=20, verbose=0, mode='auto')
model.fit(train_x,train_y,
epochs = nn_epochs,
callbacks = [callback_early_stopping,cb_lr_schedule, MacroF1(model, valid_x, valid_y)], # adding custom evaluation metric for each epoch
batch_size = nn_batch_size,verbose = 2,
validation_data = (valid_x,valid_y))
preds_f = model.predict(valid_x)
f1_score_ = f1_score(np.argmax(valid_y, axis=2).reshape(-1), np.argmax(preds_f, axis=2).reshape(-1), average = 'macro') # need to get the class with the biggest probability
print(f'Training fold {n_fold + 1} completed. macro f1 score : {f1_score_ :1.5f}')
preds_f = preds_f.reshape(-1, preds_f.shape[-1])
oof_[val_orig_idx,:] += preds_f
te_preds = model.predict(test)
te_preds = te_preds.reshape(-1, te_preds.shape[-1])
preds_ += te_preds / SPLITS
del train_x, train_y, valid_x, valid_y
# calculate the oof macro f1_score
f1_score_ = f1_score(np.argmax(train_tr, axis = 2).reshape(-1), np.argmax(oof_, axis = 1), average = 'macro') # axis 2 for the 3 Dimension array and axis 1 for the 2 Domension Array (extracting the best class)
print(f'Training completed. oof macro f1 score : {f1_score_:1.5f}')
sample_submission['open_channels'] = np.argmax(preds_, axis = 1).astype(int)
oof_pd['open_channels_pred'] = np.argmax(oof_, axis = 1).astype(int)
sample_submission.to_csv('submission_wavenet.csv', index=False, float_format='%.4f')
# ## Make Folds
# In[13]:
df_train = pd.read_csv(f'{datadir}train.csv') # 载入train.csv为dataframe
df_train['file_path'] = df_train.image.apply(
lambda x: os.path.join(f"{datadir}train_images", x)) # 加入图片的文件路径
# In[14]:
# 做5fold切分数据，并把每行归属的fold加入到df_train中
gkf = GroupKFold(n_splits=5)
df_train['fold'] = -1
for fold, (train_idx, valid_idx) in enumerate(
gkf.split(df_train, None, df_train.label_group)):
df_train.loc[valid_idx, 'fold'] = fold
# In[15]:
df_train.head()
# In[16]:
# 对label_group做一次标签编码
# 相关阅读 https://blog.csdn.net/weixin_43172660/article/details/84886470
le = LabelEncoder()
df_train.label_group = le.fit_transform(df_train.label_group)
# ## Transforms
ax.set_title("Grid Search Scores", fontsize=20, fontweight='bold')
ax.set_xlabel(name_param_1, fontsize=16)
ax.set_ylabel('CV Average Score', fontsize=16)
ax.legend(loc="best", fontsize=15)
ax.grid(True)
# load the data
data = sgl.load_watch()
X = data['X']
y = data['y']
g = data['subject']
# use subject id to group folds
splitter = GroupKFold(n_splits=3)
cv = splitter.split(X, y, groups=g)
# create a feature representation pipeline
pipe = sgl.Pype([('seg', sgl.SegmentX()), ('features', sgl.FeatureRep()),
('scaler', StandardScaler()),
('rf', RandomForestClassifier())])
# create a parameter dictionary using the sklearn API
# note that if you want to set a parameter to a single value, it will still need to be as a list
par_grid = {
'seg__width': [50, 100, 200],
'seg__overlap': [0., 0.5],
'rf__n_estimators': [20]
}
logger.info('{} Features after dropping null columns'.format(
len(X_type.columns)))
# Start training for type
bond_start = timer()
fold_count = 1
# Train the model
# X_type = X.loc[X['type'] == bond_type]
# y_type = y.iloc[X_type.index]
# X_test_type = X_test.loc[X_test['type'] == bond_type]
mol_group_type = mol_group.loc[mol_group['type'] ==
bond_type]['molecule_name']
oof = np.zeros(len(X_type))
prediction_type = np.zeros(len(X_test_type))
bond_scores = []
for fold_n, (train_idx, valid_idx) in enumerate(
folds.split(X_type, groups=mol_group_type)):
if MODEL_TYPE == 'lgbm':
fold_start = timer()
logger.info('Running Type {} - Fold {} of {}'.format(
bond_type, fold_count, folds.n_splits))
X_train, X_valid = X_type.iloc[train_idx], X_type.iloc[valid_idx]
y_train, y_valid = y_type.iloc[train_idx], y_type.iloc[valid_idx]
model = lgb.LGBMRegressor(**lgb_params,
n_estimators=N_ESTIMATORS,
n_jobs=N_THREADS)
model.fit(
X_train.drop('type', axis=1),
y_train,
eval_set=[ #(X_train.drop('type', axis=1), y_train),
(X_valid.drop('type', axis=1), y_valid)
],
# Step 2: Collect data for running CRF classifier
true_iob_dir = join(LOCAL_DIR, 'train', 'iob')
data = collect_crf_data(true_iob_dir, base_feats_dir, word_feats_dir)
# Step 3: Create folds
# create folds from complete texts only (i.e. instances of the same text
# are never in different folds)
# TODO How to set seed for random generator?
group_k_fold = GroupKFold(n_splits=5)
# use same split for all three entities
splits = list(
group_k_fold.split(data['feats'], data['Material'], data['filenames']))
# Step 4: Run CRF classifier
crf = PruneCRF(c1=0.1, c2=0.1, all_possible_transitions=True)
pred = {}
for ent in ENTITIES:
pred[ent] = cross_val_predict(crf, data['feats'], data[ent], cv=splits)
# Report scores directly on I and B tags,
# disregard 'O' because it is by far the most frequent class
print('\n' + ent + ':\n')
print(
flat_classification_report(data[ent],
pred[ent],
digits=3,
labels=('B', 'I')))
def test_group_kfold():
rng = np.random.RandomState(0)
# Parameters of the test
n_groups = 15
n_samples = 1000
n_splits = 5
X = y = np.ones(n_samples)
# Construct the test data
tolerance = 0.05 * n_samples # 5 percent error allowed
groups = rng.randint(0, n_groups, n_samples)
ideal_n_groups_per_fold = n_samples // n_splits
len(np.unique(groups))
# Get the test fold indices from the test set indices of each fold
folds = np.zeros(n_samples)
lkf = GroupKFold(n_splits=n_splits)
for i, (_, test) in enumerate(lkf.split(X, y, groups)):
folds[test] = i
# Check that folds have approximately the same size
assert_equal(len(folds), len(groups))
for i in np.unique(folds):
assert_greater_equal(tolerance,
abs(sum(folds == i) - ideal_n_groups_per_fold))
# Check that each group appears only in 1 fold
for group in np.unique(groups):
assert_equal(len(np.unique(folds[groups == group])), 1)
# Check that no group is on both sides of the split
groups = np.asarray(groups, dtype=object)
for train, test in lkf.split(X, y, groups):
assert_equal(len(np.intersect1d(groups[train], groups[test])), 0)
# Construct the test data
groups = np.array(['Albert', 'Jean', 'Bertrand', 'Michel', 'Jean',
'Francis', 'Robert', 'Michel', 'Rachel', 'Lois',
'Michelle', 'Bernard', 'Marion', 'Laura', 'Jean',
'Rachel', 'Franck', 'John', 'Gael', 'Anna', 'Alix',
'Robert', 'Marion', 'David', 'Tony', 'Abel', 'Becky',
'Madmood', 'Cary', 'Mary', 'Alexandre', 'David',
'Francis', 'Barack', 'Abdoul', 'Rasha', 'Xi', 'Silvia'])
n_groups = len(np.unique(groups))
n_samples = len(groups)
n_splits = 5
tolerance = 0.05 * n_samples # 5 percent error allowed
ideal_n_groups_per_fold = n_samples // n_splits
X = y = np.ones(n_samples)
# Get the test fold indices from the test set indices of each fold
folds = np.zeros(n_samples)
for i, (_, test) in enumerate(lkf.split(X, y, groups)):
folds[test] = i
# Check that folds have approximately the same size
assert_equal(len(folds), len(groups))
for i in np.unique(folds):
assert_greater_equal(tolerance,
abs(sum(folds == i) - ideal_n_groups_per_fold))
# Check that each group appears only in 1 fold
with warnings.catch_warnings():
warnings.simplefilter("ignore", DeprecationWarning)
for group in np.unique(groups):
assert_equal(len(np.unique(folds[groups == group])), 1)
# Check that no group is on both sides of the split
groups = np.asarray(groups, dtype=object)
for train, test in lkf.split(X, y, groups):
assert_equal(len(np.intersect1d(groups[train], groups[test])), 0)
# groups can also be a list
cv_iter = list(lkf.split(X, y, groups.tolist()))
for (train1, test1), (train2, test2) in zip(lkf.split(X, y, groups),
cv_iter):
assert_array_equal(train1, train2)
assert_array_equal(test1, test2)
# Should fail if there are more folds than groups
groups = np.array([1, 1, 1, 2, 2])
X = y = np.ones(len(groups))
assert_raises_regexp(ValueError, "Cannot have number of splits.*greater",
next, GroupKFold(n_splits=3).split(X, y, groups))
def test_group_kfold():
rng = np.random.RandomState(0)
# Parameters of the test
n_groups = 15
n_samples = 1000
n_splits = 5
X = y = np.ones(n_samples)
# Construct the test data
tolerance = 0.05 * n_samples # 5 percent error allowed
groups = rng.randint(0, n_groups, n_samples)
ideal_n_groups_per_fold = n_samples // n_splits
len(np.unique(groups))
# Get the test fold indices from the test set indices of each fold
folds = np.zeros(n_samples)
lkf = GroupKFold(n_splits=n_splits)
for i, (_, test) in enumerate(lkf.split(X, y, groups)):
folds[test] = i
# Check that folds have approximately the same size
assert_equal(len(folds), len(groups))
for i in np.unique(folds):
assert_greater_equal(tolerance,
abs(sum(folds == i) - ideal_n_groups_per_fold))
# Check that each group appears only in 1 fold
for group in np.unique(groups):
assert_equal(len(np.unique(folds[groups == group])), 1)
# Check that no group is on both sides of the split
groups = np.asarray(groups, dtype=object)
for train, test in lkf.split(X, y, groups):
assert_equal(len(np.intersect1d(groups[train], groups[test])), 0)
# Construct the test data
groups = np.array([
'Albert', 'Jean', 'Bertrand', 'Michel', 'Jean', 'Francis', 'Robert',
'Michel', 'Rachel', 'Lois', 'Michelle', 'Bernard', 'Marion', 'Laura',
'Jean', 'Rachel', 'Franck', 'John', 'Gael', 'Anna', 'Alix', 'Robert',
'Marion', 'David', 'Tony', 'Abel', 'Becky', 'Madmood', 'Cary', 'Mary',
'Alexandre', 'David', 'Francis', 'Barack', 'Abdoul', 'Rasha', 'Xi',
'Silvia'
])
n_groups = len(np.unique(groups))
n_samples = len(groups)
n_splits = 5
tolerance = 0.05 * n_samples # 5 percent error allowed
ideal_n_groups_per_fold = n_samples // n_splits
X = y = np.ones(n_samples)
# Get the test fold indices from the test set indices of each fold
folds = np.zeros(n_samples)
for i, (_, test) in enumerate(lkf.split(X, y, groups)):
folds[test] = i
# Check that folds have approximately the same size
assert_equal(len(folds), len(groups))
for i in np.unique(folds):
assert_greater_equal(tolerance,
abs(sum(folds == i) - ideal_n_groups_per_fold))
# Check that each group appears only in 1 fold
with warnings.catch_warnings():
warnings.simplefilter("ignore", DeprecationWarning)
for group in np.unique(groups):
assert_equal(len(np.unique(folds[groups == group])), 1)
# Check that no group is on both sides of the split
groups = np.asarray(groups, dtype=object)
for train, test in lkf.split(X, y, groups):
assert_equal(len(np.intersect1d(groups[train], groups[test])), 0)
# groups can also be a list
cv_iter = list(lkf.split(X, y, groups.tolist()))
for (train1, test1), (train2, test2) in zip(lkf.split(X, y, groups),
cv_iter):
assert_array_equal(train1, train2)
assert_array_equal(test1, test2)
# Should fail if there are more folds than groups
groups = np.array([1, 1, 1, 2, 2])
X = y = np.ones(len(groups))
assert_raises_regexp(ValueError, "Cannot have number of splits.*greater",
next,
GroupKFold(n_splits=3).split(X, y, groups))