def tabtune(Xtrain,
Xvalid,
ytrain,
yvalid,
verbose=True,
scorefunc=roc_auc_score,
predfunc=pred_1dprobs,
return_extras=False,
return_score=False,
**kwargs):
for k in DEFAULT_TAB:
if k not in kwargs:
kwargs[k] = DEFAULT_TAB[k]
xg_bestimator = None
xg_best_score = -float('inf')
xg_best_params = None
xg_params_scores = {}
xg_results = {}
params = TUNE_TAB.copy()
xg_keys = params.keys()
for vals in tqdm(list(itertools.product(*[params[k] for k in xg_keys]))):
paramdict = {k: v for k, v in zip(xg_keys, vals)}
n_d_a = paramdict.pop('n_d_a')
paramdict['n_d'] = paramdict['n_a'] = n_d_a
xg_results[vals] = {}
bst = TabNetClassifier(**kwargs, **paramdict)
bst.fit(Xtrain,
ytrain,
eval_set=[(Xtrain, ytrain), (Xvalid, yvalid)],
eval_metric=['auc'],
max_epochs=MAX_EPOCHS,
patience=20,
batch_size=1024,
virtual_batch_size=128,
num_workers=0,
weights=1,
drop_last=False) #,eval_set=[(Xvalid,yvalid)]
cur_score = scorefunc(yvalid, predfunc(bst, Xvalid))
xg_params_scores[vals] = cur_score
if cur_score > xg_best_score:
xg_bestimator = bst
xg_best_score = cur_score
xg_best_params = paramdict
# xg_best_params['n_estimators']=bst.booster_.best_iteration
if not return_extras:
return (xg_best_params,
xg_best_score) if return_score else xg_best_params
else:
return ((xg_best_params, xg_best_score), xg_params_scores)
def build_tabnet():
model_file_name = 'tabnet_model_{}'.format(current_dataset_name)
df = current_dataset.copy()
cleaning_text(df)
X = df['clean_content']
y = df['emotion']
# tokenize la data
tok = Tokenizer(num_words=1000, oov_token='<UNK>')
# fit le model avec les données de train
# tok.fit_on_texts(X)
# X = tok.texts_to_matrix(X, mode='tfidf')
# split data
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.33,
stratify=y,
random_state=1)
X_test_save = X_test
tok.fit_on_texts(X_test)
X_test = tok.texts_to_matrix(X_test, mode='tfidf')
tok.fit_on_texts(X_train)
X_train = tok.texts_to_matrix(X_train, mode='tfidf')
# X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.3, stratify=y)
# build model, fit and predict
if LOAD_MODEL and pathlib.Path(model_file_name).exists():
model = pickle.load(open(model_file_name, 'rb'))
else:
model = TabNetClassifier()
model.fit(X_train=X_train,
y_train=y_train,
eval_set=[(X_train, y_train), (X_test, y_test)],
eval_name=['train', 'valid'],
eval_metric=['accuracy', 'balanced_accuracy', 'logloss'])
preds_mapper = {
idx: class_name
for idx, class_name in enumerate(model.classes_)
}
preds = model.predict_proba(X_test)
y_pred_proba = np.vectorize(preds_mapper.get)(np.argmax(preds, axis=1))
y_pred = model.predict(X_test)
test_acc = accuracy_score(y_pred=y_pred, y_true=y_test)
pickle.dump(model, open(model_file_name, 'wb'))
# model.save_model(model_file_name)
return model, y_test, y_pred, test_acc
def fit(self, X, y):
X, y = check_X_y(X, y)
self.estimators_ = []
self.features_ = []
self.classes_ = np.unique(y)
self.n_samples_ = int(np.round(self.max_samples * X.shape[0]))
self.n_features_ = int(np.round(self.max_features * X.shape[1]))
for _ in range(self.n_estimators):
samples = np.random.choice(X.shape[0],
size=self.n_samples_,
replace=self.bootstrap)
features = np.random.choice(X.shape[1],
size=self.n_features_,
replace=False)
unused_samples = np.array(
[i for i in range(X.shape[0]) if i not in samples])
X_train = X[samples][:, features]
y_train = y[samples]
estimator = TabNetClassifier(verbose=self.verbose,
device_name=self.device_name)
if self.oob_score and len(unused_samples) > 0:
X_val = X[unused_samples][:, features]
y_val = y[unused_samples]
estimator.fit(X_train,
y_train,
eval_set=[(X_val, y_val)],
eval_metric=['balanced_accuracy'],
patience=self.patience,
max_epochs=self.max_epochs)
else:
estimator.fit(X_train,
y_train,
patience=self.patience,
max_epochs=self.max_epochs)
self.estimators_.append(estimator)
self.features_.append(features)
return self
class ModelTabNetClassifier(Model):
def train(self, tr_x, tr_y, va_x=None, va_y=None, te_x=None):
categorical_dims = {}
for col in self.categorical_features:
tr_x[col] = tr_x[col].fillna("unk")
va_x[col] = va_x[col].fillna("unk")
te_x[col] = te_x[col].fillna("unk")
categorical_dims[col] = len(set(tr_x[col].values) | set(va_x[col].values) | set(te_x[col].values))
cat_idxs = [i for i, f in enumerate(tr_x.columns) if f in self.categorical_features]
cat_dims = [categorical_dims[f] for i, f in enumerate(tr_x.columns) if f in self.categorical_features]
cat_emb_dim = [10 for _ in categorical_dims]
for col in tr_x.columns:
tr_x[col] = tr_x[col].fillna(tr_x[col].mean())
va_x[col] = va_x[col].fillna(tr_x[col].mean())
te_x[col] = te_x[col].fillna(tr_x[col].mean())
self.model = TabNetClassifier(cat_dims=cat_dims, cat_emb_dim=cat_emb_dim, cat_idxs=cat_idxs)
self.model.fit(X_train=tr_x.values, y_train=tr_y.values,
X_valid=va_x.values, y_valid=va_y.values,
max_epochs=1000,
patience=50,
batch_size=1024,
virtual_batch_size=128)
def predict(self, te_x):
return self.model.predict_proba(te_x.values)[:, 1].reshape(-1, )
def save_model(self):
model_path = os.path.join('../output/model', f'{self.run_fold_name}.model')
os.makedirs(os.path.dirname(model_path), exist_ok=True)
Data.dump(self.model, model_path)
def load_model(self):
model_path = os.path.join('../output/model', f'{self.run_fold_name}.model')
self.model = Data.load(model_path)
clf = TabNetClassifier(
optimizer_fn=torch.optim.Adam,
optimizer_params=dict(lr=2e-1),
scheduler_params={"step_size": 10, "gamma": 0.9}, # how to use learning rate scheduler
scheduler_fn=torch.optim.lr_scheduler.StepLR,
mask_type="sparsemax", # This will be overwritten if using pretrain model
)
clf.fit(
X_train=X_trn,
y_train=y_trn,
eval_set=[(X_trn, y_trn), (X_vld, y_vld)],
eval_name=["train", "valid"],
eval_metric=["logloss"],
max_epochs=max_epochs,
patience=50,
batch_size=128,
virtual_batch_size=128,
num_workers=0,
weights=1,
drop_last=False,
from_unsupervised=loaded_pretrain,
)
fold_preds = clf.predict_proba(X_vld).astype(np.float64)[:, 1]
_test_preds.append(clf.predict_proba(X_tst)[:, 1])
oof[vld_index] = fold_preds
scores.append(log_loss(y_vld, fold_preds))
importances = pd.concat(
[
importances,
optimizer_params=dict(lr=2e-2),
scheduler_params={"step_size":50, # how to use learning rate scheduler
"gamma":0.9},
scheduler_fn=torch.optim.lr_scheduler.StepLR,
mask_type='entmax' # "sparsemax"
)
X_train = train[features].values[train_indices]
y_train = train[target].values[train_indices]
X_valid = train[features].values[valid_indices]
y_valid = train[target].values[valid_indices]
X_test = train[features].values[test_indices]
y_test = train[target].values[test_indices]
max_epochs = 1000 if not os.getenv("CI", False) else 2
clf.fit(
X_train=X_train, y_train=y_train,
eval_set=[(X_train, y_train), (X_valid, y_valid)],
eval_name=['train', 'valid'],
eval_metric=['auc'],
max_epochs=max_epochs , patience=20,
batch_size=1024, virtual_batch_size=128,
num_workers=0,
weights=1,
drop_last=False
)
d_all[col] = preprocessing.LabelEncoder().fit_transform(d_all[col])
X_all = d_all[vars_num+vars_cat]
y_all = np.where(d_all["dep_delayed_15min"]=="Y",1,0)
cat_idxs = [ i for i, col in enumerate(X_all.columns) if col in vars_cat]
cat_dims = [ len(np.unique(X_all.iloc[:,i].values)) for i in cat_idxs]
X_train = X_all[0:d_train.shape[0]].to_numpy()
y_train = y_all[0:d_train.shape[0]]
X_test = X_all[d_train.shape[0]:(d_train.shape[0]+d_test.shape[0])].to_numpy()
y_test = y_all[d_train.shape[0]:(d_train.shape[0]+d_test.shape[0])]
md = TabNetClassifier(cat_idxs=cat_idxs,
cat_dims=cat_dims,
cat_emb_dim=1
)
%%time
md.fit( X_train=X_train, y_train=y_train,
max_epochs=10, patience=0
)
y_pred = md.predict_proba(X_test)[:,1]
print(metrics.roc_auc_score(y_test, y_pred))
class MyTabNetClassifierModel(BaseModel):
"""
Paramters
---------
ref: https://dreamquark-ai.github.io/tabnet/generated_docs/README.html#model-parameters
model_params:
n_d:default=8(range 8 to 64)
n_a:default=8
n_steps:default=3(range 3 to 10)
gamma:default=1.3(range 1.0 to 2.0)
n_independent:default=2(range 1 to 5)
n_shared:default=2(range 1 to 5)
lambda_sparse:default=1e3
optimizer_fn:default=Adam
optimizer_params:default=(lr=2e2, weight_decay=None),
mask_type:default=sparsemax or entmax
scheduler_params:dict(T_0=200, T_mult=1, eta_min=1e-4, last_epoch=-1, verbose=False),
seed: default=0
verbose=5,
cat_dims=cat_dims, cat_idxs=cat_idx, cat_emb_dim=1
fit_params:
max_epochs:default=200
patience:default=15
loss_fn(torch.loss or list of torch.loss):default to mse for regression and cross entropy for classification
eval_metric(list or str)
batch_size:default=1024
virtual_batch_size:default=128
pretrain_ratio
### Example use:
>>>nunique = train_feat_df.nunique()
>>>types = train_feat_df.dtypes
>>>categorical_columns = []
>>>categorical_dims = {}
>>>train_feat_df["is_train"] = 1
>>>test_feat_df["is_train"] = 0
>>>all_df = pd.concat([train_feat_df, test_feat_df])
>>for col in train_feat_df.drop(["is_train"], axis=1).columns:
if str(types[col]) == 'category' or nunique[col] < 200:
l_enc = LabelEncoder()
all_df[col] = l_enc.fit_transform(all_df[col].values)
all_df[col] = all_df[col].astype("category")
categorical_columns.append(col)
categorical_dims[col] = len(l_enc.classes_ )
>>>cat_idx = [i for i, f in enumerate(train_feat_df.columns.tolist()) if f in categorical_columns]
>>>cat_dims = [categorical_dims[f] for i, f in enumerate(train_feat_df.columns.tolist()) if f in categorical_columns]
"""
def __init__(self, model_params, fit_params):
self.model_params = model_params
self.fit_params = fit_params
def build_model(self):
self.model = TabNetClassifier(**self.model_params)
return self.model
def fit(self, train_x, train_y, valid_x=None, valid_y=None):
train_x = train_x.values
valid_x = valid_x.values
self.model = self.build_model()
self.model.fit(train_x,
train_y,
eval_set=[(train_x, train_y), (valid_x, valid_y)],
eval_name=["train", "valid"],
**self.fit_params)
return self.model
def predict(self, est, valid_x):
valid_x = valid_x.values
preds = est.predict_proba(valid_x)[:, 1]
return preds
def get_feature_importance(self,
train_x: pd.DataFrame,
is_save=False,
filepath=None):
feature_importance_df = pd.DataFrame()
num = 0
for i, model in self.models.items():
_df = pd.DataFrame()
_df['feature_importance'] = model.feature_importances_
_df['column'] = train_x.columns
_df['fold'] = num + 1
feature_importance_df = pd.concat([feature_importance_df, _df],
axis=0,
ignore_index=True)
num += 1
order = feature_importance_df.groupby('column')\
.sum()[['feature_importance']]\
.sort_values('feature_importance', ascending=False).index[:50]
fig, ax = plt.subplots(figsize=(8, max(6, len(order) * .25)))
if is_save:
fig.savefig(filepath + "tabnet_feature_importance.png")
_df.to_csv(filepath + "tabnet_feature_importance.csv", index=False)
sns.boxenplot(data=feature_importance_df,
x='feature_importance',
y='column',
order=order,
ax=ax,
palette='viridis',
orient='h')
ax.tick_params(axis='x', rotation=90)
ax.set_title('Tabnet Feature Importance')
ax.grid()
plt.show()
MAX_EPOCH = 10
BS = 1024
md = TabNetClassifier(cat_idxs=cat_idxs,
cat_dims=cat_dims,
cat_emb_dim=1,
## optimizer_fn=torch.optim.Adam,
## optimizer_params=dict(lr=2e-2),
scheduler_fn=torch.optim.lr_scheduler.OneCycleLR,
scheduler_params=dict(max_lr=0.05,
steps_per_epoch=int(X_train.shape[0] / BS),
epochs=MAX_EPOCH,
is_batch_level=True),
mask_type='entmax' # "sparsemax"
)
%%time
md.fit( X_train=X_train, y_train=y_train,
max_epochs=MAX_EPOCH, patience=0,
## batch_size=1024, virtual_batch_size=128,
## weights=0,
drop_last = True
)
y_pred = md.predict_proba(X_test)[:,1]
print(metrics.roc_auc_score(y_test, y_pred))
def main():
# Generate Synthetic Data
data, test_data, cat_col_names, num_col_names = data_load()
cat_dims = data[cat_col_names].nunique().to_list()
cat_idxs = [(cat_col_names + num_col_names).index(cat_col)
for cat_col in cat_col_names]
cat_emb_dims = np.ceil(np.log(cat_dims)).astype(np.int).tolist()
cat_emb_dims = np.ceil(np.clip((np.array(cat_dims)) / 2, a_min=1,
a_max=50)).astype(np.int).tolist()
FEATURES = cat_col_names + num_col_names
df_sub = pd.read_csv('Data/sample_submission.csv')
bsize = 2500 * 2
# ##########Define the Configs############
N_D = 16
N_A = 16
N_INDEP = 2
N_SHARED = 2
N_STEPS = 1 # 2
MASK_TYPE = "sparsemax"
GAMMA = 1.5
BS = 512
MAX_EPOCH = 21 # 20
PRETRAIN = True
X = data[FEATURES].values
y = data["target"].values
X_test = test_data[FEATURES].values
if PRETRAIN:
pretrain_params = dict(
n_d=N_D,
n_a=N_A,
n_steps=N_STEPS, # 0.2,
n_independent=N_INDEP,
n_shared=N_SHARED,
cat_idxs=cat_idxs,
cat_dims=cat_dims,
cat_emb_dim=cat_emb_dims,
gamma=GAMMA,
lambda_sparse=0.,
optimizer_fn=torch.optim.Adam,
optimizer_params=dict(lr=2e-2),
mask_type=MASK_TYPE,
scheduler_params=dict(
mode="min",
patience=3,
min_lr=1e-5,
factor=0.5,
),
scheduler_fn=torch.optim.lr_scheduler.ReduceLROnPlateau,
verbose=1,
)
pretrainer = TabNetPretrainer(**pretrain_params)
pretrainer.fit(
X_train=X_test,
eval_set=[X],
max_epochs=MAX_EPOCH,
patience=25,
batch_size=BS,
virtual_batch_size=BS, # 128,
num_workers=0,
drop_last=True,
pretraining_ratio=
0.5 # The bigger your pretraining_ratio the harder it is to reconstruct
)
# Training the Model
# tabular_mode.fit(train=train, validation=val)
# # Evaluating the Model
# # #Loss and Metrics on New Data¶
# result = tabular_mode.evaluate(test)
cv = StratifiedKFold(n_splits=10, shuffle=True, random_state=777)
BS = 2048
MAX_EPOCH = 20
# skf = StratifiedKFold(n_splits=5, random_state=2021, shuffle=True)
data['oof_preds'] = np.nan
for fold_nb, (train_index, valid_index) in enumerate(cv.split(X, y)):
X_train, X_valid = X[train_index], X[valid_index]
y_train, y_valid = y[train_index], y[valid_index]
tabnet_params = dict(
n_d=N_D,
n_a=N_A,
n_steps=N_STEPS,
gamma=GAMMA,
n_independent=N_INDEP,
n_shared=N_SHARED,
lambda_sparse=1e-5,
seed=0,
clip_value=2,
cat_idxs=cat_idxs,
cat_dims=cat_dims,
cat_emb_dim=cat_emb_dims,
mask_type=MASK_TYPE,
device_name='auto',
optimizer_fn=torch.optim.Adam,
optimizer_params=dict(lr=5e-2, weight_decay=1e-5),
scheduler_params=dict(
max_lr=5e-2,
steps_per_epoch=int(X_train.shape[0] / BS),
epochs=MAX_EPOCH,
# final_div_factor=100,
is_batch_level=True),
scheduler_fn=torch.optim.lr_scheduler.OneCycleLR,
# scheduler_params=dict(mode='max',
# factor=0.5,
# patience=5,
# is_batch_level=False,),
# scheduler_fn=torch.optim.lr_scheduler.ReduceLROnPlateau,
verbose=1)
# Defining TabNet model
model = TabNetClassifier(**tabnet_params)
model.fit(
X_train=X_train,
y_train=y_train,
from_unsupervised=pretrainer if PRETRAIN else None,
eval_set=[(X_train, y_train), (X_valid, y_valid)],
eval_name=["train", "valid"],
eval_metric=["auc"],
batch_size=BS,
virtual_batch_size=256,
max_epochs=MAX_EPOCH,
drop_last=True,
pin_memory=True,
patience=10,
)
val_preds = model.predict_proba(X_valid)[:, -1]
print('auc:', roc_auc_score(y_true=y_valid, y_score=val_preds))
data['oof_preds'].iloc[valid_index] = val_preds
test_preds = model.predict_proba(X_test)[:, -1]
df_sub[f"fold_{fold_nb+1}"] = test_preds
df_sub["target"] = df_sub.filter(like="fold_").mean(axis=1).values
df_sub.to_csv("Analysis/submission_5_tabnet.csv", index=False)
df_sub = pd.read_csv("Analysis/submission_5_tabnet.csv")
# df_sub.target = df_sub.target.map(lambda x: 0 if x<=0.5 else 1)
df_sub.loc[:,
["id", "target"]].to_csv("Analysis/submission_5_2_tabnet.csv",
index=False)
scaler = StandardScaler().fit(interactions_t)
interactions_t = scaler.transform(interactions_t)
interactions_v = scaler.transform(interactions_v)
torch.manual_seed(0)
np.random.seed(0)
clf = TabNetClassifier( scheduler_params={"step_size":5, "gamma":0.8},
scheduler_fn=torch.optim.lr_scheduler.StepLR )
clf.fit(
interactions_t, y_t,
eval_set=[(interactions_t, y_t),(interactions_v, y_v)],
eval_name=['train','val'],
batch_size = 4096,
virtual_batch_size = 4096//8,
patience = 3,
max_epochs = 100
)
# print('importances:',clf.feature_importances_*100)
selected = np.where(clf.feature_importances_*100 > 8)[0]
print('multiplying by:',i)
print('selected:',selected)
print('percents:',clf.feature_importances_[selected]*100)
print()
## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
X_test = X_all[d_train.shape[0]:(d_train.shape[0]+d_test.shape[0])].to_numpy()
y_test = y_all[d_train.shape[0]:(d_train.shape[0]+d_test.shape[0])]
cat_idxs = [ i for i, col in enumerate(X_all.columns) if col in vars_cat]
cat_dims = [ len(np.unique(X_all.iloc[:,i].values)) for i in cat_idxs]
cat_emb_dim = np.floor(np.log(cat_dims)).astype(int)
md = TabNetClassifier(cat_idxs=cat_idxs,
cat_dims=cat_dims,
cat_emb_dim=cat_emb_dim,
## optimizer_fn=torch.optim.Adam,
## optimizer_params=dict(lr=2e-2),
## mask_type='sparsemax',
n_steps=1,
)
%%time
md.fit( X_train=X_train, y_train=y_train,
eval_set=[(X_train, y_train), (X_valid, y_valid)],
eval_name=['train', 'valid'],
eval_metric=['auc'],
max_epochs=20, patience=20,
## batch_size=1024, virtual_batch_size=128,
## weights=0,
)
y_pred = md.predict_proba(X_test)[:,1]
print(metrics.roc_auc_score(y_test, y_pred))
X_test = X_all[d_train.shape[0]:(d_train.shape[0]+d_test.shape[0])].to_numpy()
y_test = y_all[d_train.shape[0]:(d_train.shape[0]+d_test.shape[0])]
cat_idxs = [ i for i, col in enumerate(X_all.columns) if col in vars_cat]
cat_dims = [ len(np.unique(X_all.iloc[:,i].values)) for i in cat_idxs]
cat_emb_dim = np.floor(np.log(cat_dims)).astype(int)
md = TabNetClassifier(cat_idxs=cat_idxs,
cat_dims=cat_dims,
cat_emb_dim=cat_emb_dim,
## optimizer_fn=torch.optim.Adam,
## optimizer_params=dict(lr=2e-2),
## mask_type='sparsemax',
n_steps=1,
)
%%time
md.fit( X_train=X_train, y_train=y_train,
max_epochs=10, patience=0,
## batch_size=1024, virtual_batch_size=128,
## weights=0,
)
y_pred = md.predict_proba(X_test)[:,1]
print(metrics.roc_auc_score(y_test, y_pred))
optimizer_params=dict(lr=2e-2),
verbose=0,
scheduler_params={
"step_size": 50, # how to use learning rate scheduler
"gamma": 0.9
},
scheduler_fn=torch.optim.lr_scheduler.StepLR,
mask_type='entmax' # "sparsemax"
)
clf.fit(X_train=X_tr,
y_train=y_tr,
X_valid=X_val,
y_valid=y_val,
max_epochs=epoch,
patience=epoch,
batch_size=1024,
virtual_batch_size=128,
num_workers=0,
weights=1,
drop_last=False)
test_pred += clf.predict_proba(X_test)[:, 1]
test_pred /= 5
test_auc = roc_auc_score(y_test, test_pred)
print('test auc:', test_auc)
res[data_name] = test_auc
y_test = y_all[d_train.shape[0]:(d_train.shape[0]+d_test.shape[0])]
md = TabNetClassifier(cat_idxs=cat_idxs,
cat_dims=cat_dims,
cat_emb_dim=1,
optimizer_fn=torch.optim.Adam,
optimizer_params=dict(lr=2e-2),
scheduler_params={"step_size":50, # how to use learning rate scheduler
"gamma":0.9},
scheduler_fn=torch.optim.lr_scheduler.StepLR,
mask_type='entmax' # "sparsemax"
)
%%time
md.fit( X_train=X_train, y_train=y_train,
eval_set=[(X_train, y_train)],
eval_name=['train'],
eval_metric=['auc'],
max_epochs=10, patience=0,
batch_size=1024, virtual_batch_size=128,
num_workers=0,
weights=1,
drop_last=False
)
y_pred = md.predict_proba(X_test)[:,1]
print(metrics.roc_auc_score(y_test, y_pred))
y_test = y_all[d_train.shape[0]:(d_train.shape[0]+d_test.shape[0])]
cat_idxs = [ i for i, col in enumerate(X_all.columns) if col in vars_cat]
cat_dims = [ len(np.unique(X_all.iloc[:,i].values)) for i in cat_idxs]
cat_emb_dim = np.floor(np.log(cat_dims)).astype(int)
md = TabNetClassifier(cat_idxs=cat_idxs,
cat_dims=cat_dims,
cat_emb_dim=cat_emb_dim,
## optimizer_fn=torch.optim.Adam,
## optimizer_params=dict(lr=2e-2),
## mask_type='sparsemax',
n_steps=1,
)
%%time
md.fit( X_train=X_train, y_train=y_train,
eval_set=[(X_train, y_train), (X_test, y_test)],
eval_name=['train', 'test_EVIL'],
eval_metric=['auc'],
max_epochs=100, patience=100,
## batch_size=1024, virtual_batch_size=128,
## weights=0,
)
y_pred = md.predict_proba(X_test)[:,1]
print(metrics.roc_auc_score(y_test, y_pred))
import torch
N_D = 16
N_A = 16
N_INDEP = 2
N_SHARED = 2
N_STEPS = 1 #2
MASK_TYPE = "sparsemax"
GAMMA = 1.5
BS = 128 #512
MAX_EPOCH = 20 # 20
PRETRAIN = True
clf = TabNetClassifier()
clf.fit(
X,
y,
# eval_set=[(X_valid, y_valid)]
)
# if PRETRAIN:
# pretrain_params = dict(n_d=N_D, n_a=N_A, n_steps=N_STEPS, #0.2,
# n_independent=N_INDEP, n_shared=N_SHARED,
# cat_idxs=cat_idxs,
# cat_dims=cat_dims,
# cat_emb_dim=cat_emb_dims,
# gamma=GAMMA,
# lambda_sparse=0., optimizer_fn=torch.optim.Adam,
# optimizer_params=dict(lr=2e-2),
# mask_type=MASK_TYPE,
# scheduler_params=dict(mode="min",
# patience=3,
categorical_val.remove('answer_creation_date')
categorical_val.remove('group_creation_date')
categorical_val.remove('request_creation_date')
categorical_val.remove('victim_of_violence_type')
date_columns = [
'answer_creation_date', 'group_creation_date', 'request_creation_date'
]
# We transform the dataframe into encoded features and target
X, y = preprocess(requests_train, categorical_val, date_columns)
X_train, X_val, y_train, y_val = train_test_split(X,
y,
test_size=0.3,
random_state=42)
X_train, y_train = preprocess_for_tabnet(X_train, y_train)
X_val, y_val = preprocess_for_tabnet(X_val, y_val)
clf = TabNetClassifier(optimizer_fn=torch.optim.Adam,
optimizer_params=dict(lr=1e-1))
clf.device = device
weights = {0: 1, 1: 10, 2: 10**2, 3: 10**3}
clf.fit(
X_train=X_train,
y_train=y_train, ##Train features and train targets
X_valid=X_val,
y_valid=y_val, ##Valid features and valid targets
weights=weights,
max_epochs=20, ##Maxiµmum number of epochs during training
patience=
5, ##Number of consecutive non improving epoch before early stopping
batch_size=16, ##Training batch size
)
def fit(self, x_train, y_train, kf_splits=5, tabnet_type=None):
def _get_tabnet_params(tabnet_type):
if (tabnet_type is None):
tabnet_params = dict(
verbose=40,
optimizer_fn=torch.optim.Adam,
optimizer_params=dict(lr=1e-2, weight_decay=1e-5),
scheduler_params=dict(max_lr=0.05,
steps_per_epoch=x_train.shape[0] //
128,
epochs=300),
scheduler_fn=torch.optim.lr_scheduler.OneCycleLR)
fit_params = dict(batch_size=1024,
virtual_batch_size=128,
eval_metric='accuracy')
elif (tabnet_type == 'TabNet-S'):
tabnet_params = dict(
n_d=8,
n_a=8,
lambda_sparse=0.0001,
momentum=0.1,
n_steps=3,
gamma=1.2,
verbose=40,
optimizer_fn=torch.optim.Adam,
optimizer_params=dict(lr=0.01),
scheduler_params=dict(step_size=8000, gamma=0.05),
scheduler_fn=torch.optim.lr_scheduler.StepLR)
fit_params = dict(batch_size=4096,
virtual_batch_size=256,
eval_metric='mse')
else:
print('[ERROR] Unknown tabnet_type: {}'.format(tabnet_type))
quit()
# --- check problem ---
if fit_params['eval_metric'] in [
'auc', 'accuracy', 'balanced_accuracy', 'logloss'
]:
problem = 'classification'
elif fit_params['eval_metric'] in ['mse', 'mae', 'rmse', 'rmsle']:
problem = 'regression'
return tabnet_params, fit_params, problem
kf = KFold(n_splits=kf_splits, shuffle=False)
scores = []
self.tabnet_models = []
tabnet_params, fit_params, problem = _get_tabnet_params(tabnet_type)
for i, (train_index,
val_index) in enumerate(kf.split(x_train, y_train)):
if (problem == 'classification'):
unsupervised_model = TabNetPretrainer(**tabnet_params)
tabnet_model = TabNetClassifier(**tabnet_params)
elif (problem == 'regression'):
unsupervised_model = TabNetPretrainer(**tabnet_params)
tabnet_model = TabNetRegressor(**tabnet_params)
else:
pring('[ERROR] Unknown problem: {}'.format(problem))
quit()
x_tr = x_train[train_index]
x_val = x_train[val_index]
y_tr = y_train[train_index]
y_val = y_train[val_index]
unsupervised_model.fit(x_tr,
eval_set=[x_val],
patience=300,
max_epochs=5000,
pretraining_ratio=0.8)
tabnet_model.fit(
x_tr,
y_tr,
eval_set=[(x_val, y_val)],
eval_metric=[fit_params['eval_metric']],
batch_size=fit_params['batch_size'],
virtual_batch_size=fit_params['virtual_batch_size'],
patience=300,
max_epochs=5000,
from_unsupervised=unsupervised_model)
self.tabnet_models.append(tabnet_model)
prediction = tabnet_model.predict(x_val)
if (problem == 'classification'):
scores.append(accuracy_score(y_val, prediction))
elif (problem == 'regression'):
scores.append(mean_squared_error(y_val, prediction))
else:
pring('[ERROR] Unknown problem: {}'.format(problem))
quit()
if (i == 0):
feature_importances = tabnet_model.feature_importances_.copy()
else:
feature_importances = np.vstack(
(feature_importances, tabnet_model.feature_importances_))
print(scores)
print(np.mean(scores))
return scores, feature_importances
