def fit(self):
"""
perform model fitting
"""
# initialize
y_vals = np.zeros((self.train_df.shape[0], ))
if self.task == "multiclass":
n_class = len(np.unique(self.train_df[self.target].values))
oof_pred = np.zeros((self.train_df.shape[0], n_class))
y_pred = np.zeros((self.test_df.shape[0], n_class))
else:
oof_pred = np.zeros((self.train_df.shape[0], ))
y_pred = np.zeros((self.test_df.shape[0], ))
# group does not kick in when group k fold is used
if self.group is not None:
if self.group in self.features:
self.features.remove(self.group)
if self.group in self.categoricals:
self.categoricals.remove(self.group)
fi = np.zeros((self.n_splits, len(self.features)))
# target encoding
numerical_features = [f for f in self.features if f not in self.categoricals]
if self.target_encoding:
# perform target encoding
k = 0
f = 1
overall_mean = self.train_df[self.target].mean()
for c in self.categoricals:
data_tmp = pd.DataFrame({c: self.train_df[c].values, 'target': self.train_df[self.target].values})
tmp = np.nan * np.ones(self.train_df.shape[0])
cv = self.get_cv()
for fold, (train_idx, val_idx) in enumerate(cv):
# target mean
target_mean = data_tmp.iloc[train_idx].groupby(c)['target'].mean().reset_index()
# smoothing
target_count = data_tmp.iloc[train_idx].groupby(c)['target'].count().reset_index()
target_count['target'] = target_count['target'].apply(lambda x : 1 / (1 + np.exp((-x -k) / f))
target_mean = L * target_mean + (1 - L) * overall_mean
# allocate
tmp[val_idx] = self.train_df[c].iloc[val_idx].map(target_mean).values
self.train_df[c] = tmp
# replace categorical variable in test
target_mean = data_tmp.groupby(c)['target'].mean()
self.test_df.loc[:, c] = self.test_df[c].map(target_mean).values
# no categoricals any more
numerical_features = self.features.copy()
self.categoricals = []
# fill nan
if self.model not in ['lgb', 'catb', 'xgb']:
# fill NaN (numerical features -> median, categorical features -> mode)
self.train_df[numerical_features] = self.train_df[numerical_features].replace([np.inf, -np.inf], np.nan)
self.test_df[numerical_features] = self.test_df[numerical_features].replace([np.inf, -np.inf], np.nan)
self.train_df[numerical_features] = self.train_df[numerical_features].fillna(self.train_df[numerical_features].median())
self.test_df[numerical_features] = self.test_df[numerical_features].fillna(self.test_df[numerical_features].median())
self.train_df[self.categoricals] = self.train_df[self.categoricals].fillna(self.train_df[self.categoricals].mode().iloc[0])
self.test_df[self.categoricals] = self.test_df[self.categoricals].fillna(self.test_df[self.categoricals].mode().iloc[0])
# scaling, if necessary
if self.scaler is not None:
# to normal
pt = QuantileTransformer(n_quantiles=100, random_state=self.seed, output_distribution="normal")
self.train_df[numerical_features] = pt.fit_transform(self.train_df[numerical_features])
self.test_df[numerical_features] = pt.transform(self.test_df[numerical_features])
# starndardize
if self.scaler == "MinMax":
scaler = MinMaxScaler()
elif self.scaler == "Standard":
scaler = StandardScaler()
self.train_df[numerical_features] = scaler.fit_transform(self.train_df[numerical_features])
self.test_df[numerical_features] = scaler.transform(self.test_df[numerical_features])
x_test = self.test_df.copy()
if self.model == "nn":
x_test = [np.absolute(x_test[i]) for i in self.categoricals] + [x_test[numerical_features]]
else:
x_test = x_test[self.features]
else:
x_test = self.test_df[self.features]
# fitting with out of fold
cv = self.get_cv()
for fold, (train_idx, val_idx) in enumerate(cv):
# train test split
x_train, x_val = self.train_df[self.features].iloc[train_idx], self.train_df[self.features].iloc[val_idx]
y_train, y_val = self.train_df[self.target].iloc[train_idx], self.train_df[self.target].iloc[val_idx]
if self.model == "nn":
x_train = [np.absolute(x_train[i]) for i in self.categoricals] + [x_train[numerical_features]]
x_val = [np.absolute(x_val[i]) for i in self.categoricals] + [x_val[numerical_features]]
# model fitting
train_set, val_set = self.convert_dataset(x_train, y_train, x_val, y_val)
model, importance = self.train_model(train_set, val_set)
fi[fold, :] = importance
y_vals[val_idx] = y_val
# predictions and check cv score
oofs, ypred = get_oof_ypred(model, x_val, x_test, self.model, self.task)
y_pred += ypred.reshape(y_pred.shape) / self.n_splits
if self.task == "multiclass":
oof_pred[val_idx, :] = oofs.reshape(oof_pred[val_idx, :].shape)
print('Partial score of fold {} is: {}'.format(fold, self.calc_metric(y_vals[val_idx],
np.argmax(oof_pred[val_idx, :], axis=1))))
else:
oof_pred[val_idx] = oofs.reshape(oof_pred[val_idx].shape)
print('Partial score of fold {} is: {}'.format(fold, self.calc_metric(y_vals[val_idx],
oof_pred[val_idx])))
# feature importance data frame
fi_df = pd.DataFrame()
for n in np.arange(self.n_splits):
tmp = pd.DataFrame()
tmp["features"] = self.features
tmp["importance"] = fi[n, :]
tmp["fold"] = n
fi_df = pd.concat([fi_df, tmp], ignore_index=True)
gfi = fi_df[["features", "importance"]].groupby(["features"]).mean().reset_index()
fi_df = fi_df.merge(gfi, on="features", how="left", suffixes=('', '_mean'))
# outputs
if self.task == "multiclass":
loss_score = self.calc_metric(y_vals, np.argmax(oof_pred, axis=1))
else:
loss_score = self.calc_metric(y_vals, oof_pred)
if self.verbose:
print('Our oof loss score is: ', loss_score)
return y_pred, loss_score, model, oof_pred, y_vals, fi_df
def plot_feature_importance(self, rank_range=[1, 50]):
"""
function for plotting feature importance (nothing is returned when the model is NN)
:EXAMPLE:
# fit LGB regression model
model = RunModel(train_df, test_df, target, features, categoricals=categoricals,
model="lgb", task="regression", n_splits=4, cv_method="KFold",
group=None, seed=1220, scaler=None)
# plot
fi_df = model.plot_feature_importance(rank_range=[1, 100])
"""
# plot feature importance
_, ax = plt.subplots(1, 1, figsize=(10, 20))
sorted_df = self.fi_df.sort_values(by = "importance_mean", ascending=False).reset_index().iloc[self.n_splits * (rank_range[0]-1) : self.n_splits * rank_range[1]]
sns.barplot(data=sorted_df, x ="importance", y ="features", orient='h')
ax.set_xlabel("feature importance")
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
return sorted_df
def fit(self):
"""
perform model fitting
"""
# initialize
y_vals = np.zeros((self.train_df.shape[0], ))
if self.task == "multiclass":
n_class = len(np.unique(self.train_df[self.target].values))
oof_pred = np.zeros((self.train_df.shape[0], n_class))
y_pred = np.zeros((self.test_df.shape[0], n_class))
else:
oof_pred = np.zeros((self.train_df.shape[0], ))
y_pred = np.zeros((self.test_df.shape[0], ))
# group does not kick in when group k fold is used
if self.group is not None:
if self.group in self.features:
self.features.remove(self.group)
if self.group in self.categoricals:
self.categoricals.remove(self.group)
fi = np.zeros((self.n_splits, len(self.features)))
# target encoding
numerical_features = [f for f in self.features if f not in self.categoricals]
if self.target_encoding:
# perform target encoding
overall_mean = self.train_df[self.target].mean()
for c in self.categoricals:
data_tmp = pd.DataFrame({c: self.train_df[c].values, 'target': self.train_df[self.target].values})
tmp = np.nan * np.ones(self.train_df.shape[0])
cv = self.get_cv()
for fold, (train_idx, val_idx) in enumerate(cv):
# target mean
target_mean = data_tmp.iloc[train_idx].groupby(c)['target'].mean()
# smoothing
target_count = data_tmp.iloc[train_idx].groupby(c)['target'].count()
n_sigmoid = (1 / 1 + np.exp(-))
target_mean = L * target_mean + (1 - L) * overall_mean
# allocate
tmp[val_idx] = self.train_df[c].iloc[val_idx].map(target_mean).values
self.train_df[c] = tmp
# replace categorical variable in test
target_mean = data_tmp.groupby(c)['target'].mean()
self.test_df.loc[:, c] = self.test_df[c].map(target_mean).values
# no categoricals any more
numerical_features = self.features.copy()
self.categoricals = []
# fill nan
if self.model not in ['lgb', 'catb', 'xgb']:
# fill NaN (numerical features -> median, categorical features -> mode)
self.train_df[numerical_features] = self.train_df[numerical_features].replace([np.inf, -np.inf], np.nan)
self.test_df[numerical_features] = self.test_df[numerical_features].replace([np.inf, -np.inf], np.nan)
self.train_df[numerical_features] = self.train_df[numerical_features].fillna(self.train_df[numerical_features].median())
self.test_df[numerical_features] = self.test_df[numerical_features].fillna(self.test_df[numerical_features].median())
self.train_df[self.categoricals] = self.train_df[self.categoricals].fillna(self.train_df[self.categoricals].mode().iloc[0])
self.test_df[self.categoricals] = self.test_df[self.categoricals].fillna(self.test_df[self.categoricals].mode().iloc[0])
# scaling, if necessary
if self.scaler is not None:
# to normal
pt = QuantileTransformer(n_quantiles=100, random_state=self.seed, output_distribution="normal")
self.train_df[numerical_features] = pt.fit_transform(self.train_df[numerical_features])
self.test_df[numerical_features] = pt.transform(self.test_df[numerical_features])
# starndardize
if self.scaler == "MinMax":
scaler = MinMaxScaler()
elif self.scaler == "Standard":
scaler = StandardScaler()
self.train_df[numerical_features] = scaler.fit_transform(self.train_df[numerical_features])
self.test_df[numerical_features] = scaler.transform(self.test_df[numerical_features])
x_test = self.test_df.copy()
if self.model == "nn":
x_test = [np.absolute(x_test[i]) for i in self.categoricals] + [x_test[numerical_features]]
else:
x_test = x_test[self.features]
else:
x_test = self.test_df[self.features]
# fitting with out of fold
cv = self.get_cv()
for fold, (train_idx, val_idx) in enumerate(cv):
# train test split
x_train, x_val = self.train_df[self.features].iloc[train_idx], self.train_df[self.features].iloc[val_idx]
y_train, y_val = self.train_df[self.target].iloc[train_idx], self.train_df[self.target].iloc[val_idx]
if self.model == "nn":
x_train = [np.absolute(x_train[i]) for i in self.categoricals] + [x_train[numerical_features]]
x_val = [np.absolute(x_val[i]) for i in self.categoricals] + [x_val[numerical_features]]
# model fitting
train_set, val_set = self.convert_dataset(x_train, y_train, x_val, y_val)
model, importance = self.train_model(train_set, val_set)
fi[fold, :] = importance
y_vals[val_idx] = y_val
# predictions and check cv score
oofs, ypred = get_oof_ypred(model, x_val, x_test, self.model, self.task)
y_pred += ypred.reshape(y_pred.shape) / self.n_splits
if self.task == "multiclass":
oof_pred[val_idx, :] = oofs.reshape(oof_pred[val_idx, :].shape)
print('Partial score of fold {} is: {}'.format(fold, self.calc_metric(y_vals[val_idx],
np.argmax(oof_pred[val_idx, :], axis=1))))
else:
oof_pred[val_idx] = oofs.reshape(oof_pred[val_idx].shape)
print('Partial score of fold {} is: {}'.format(fold, self.calc_metric(y_vals[val_idx],
oof_pred[val_idx])))
# feature importance data frame
fi_df = pd.DataFrame()
for n in np.arange(self.n_splits):
tmp = pd.DataFrame()
tmp["features"] = self.features
tmp["importance"] = fi[n, :]
tmp["fold"] = n
fi_df = pd.concat([fi_df, tmp], ignore_index=True)
gfi = fi_df[["features", "importance"]].groupby(["features"]).mean().reset_index()
fi_df = fi_df.merge(gfi, on="features", how="left", suffixes=('', '_mean'))
# outputs
if self.task == "multiclass":
loss_score = self.calc_metric(y_vals, np.argmax(oof_pred, axis=1))
else:
loss_score = self.calc_metric(y_vals, oof_pred)
if self.verbose:
print('Our oof loss score is: ', loss_score)
return y_pred, loss_score, model, oof_pred, y_vals, fi_df