def bad_model(df, train_ixs, feats):
train = df.iloc[train_ixs, :]
model = LinearRegression(fit_intercept=False,
normalize=True,
copy_X=True,
n_jobs=-1)
model.fit(train[feats], train['logerror'])
print nptools.get_mae_loss(train['logerror'].values,
model.predict(train[feats]))
return model.predict(df[feats])
def optim_func(weights, preds, targets):
final_prediction = ktools.ensemble_preds(preds, weights)
score = 1000000 * tools.get_mae_loss(final_prediction, targets)
return score
cache_dir + 'ps2_test_2ndx{}_f{}.pkl'.format(n_models, n_folds))
print('XGBoost... ')
params = model_params.get_lvl2()
dtrain = xgb.DMatrix(new_train.values, train_targets)
dtest = xgb.DMatrix(new_test.values)
preds_train_xgb = np.zeros(len(new_train))
preds_test_xgb = np.zeros(len(new_test))
n_bags = 5
for i in range(n_bags):
model = xgb.train(params, dtrain, num_boost_round=300, verbose_eval=2)
preds_train_xgb += model.predict(dtrain)
preds_test_xgb += model.predict(dtest)
preds_train_xgb /= n_bags
preds_test_xgb /= n_bags
score = tools.get_mae_loss(train_targets, preds_train_xgb)
print('train score:{}'.format(score))
# ############Keras
print('nnet... ')
x_train = new_train.values
x_test = new_test.values
model = model_params.get_lvl2nn(x_train.shape[1])
batch_size = 256
epochs = 10
history = model.fit(x_train,
train_targets,
nb_epoch=epochs,
batch_size=batch_size)
model.history = history
preds_train_nn = model.predict(x_train).squeeze()
df = data.select_features(df)
df = df.drop(['assessmentyear'], axis=1)
print df.columns
if cv_flag:
df_full_train, targets, df_test = data.split_data(df, logerror)
df_train, df_val, train_targets, val_targets = data.split_cv(df_full_train, targets, cv_split_ratio)
cv_preds = np.repeat(0., len(df_val))
for i in range(n_bags):
x_train, x_val = tools.normalise_data(df_train.values, df_val.values)
model = model_params.get_keras(x_train.shape[1])
history = model.fit(
x_train, train_targets,
nb_epoch=epochs, batch_size=batch_size,
validation_data=(x_val, val_targets), verbose=2)
model.history = history
cv_preds += model.predict(x_val).squeeze()
cv_preds /= float(n_bags)
mae = tools.get_mae_loss(val_targets, cv_preds)
mse = mean_squared_error(val_targets, cv_preds)
msg = 'mae: {}, mse: {}, keras! train_data ratio: {}, bags:{}, epochs:{}'.format(mae, mse, cv_split_ratio, n_bags, epochs)
print(msg), logger.debug(msg)
else:
print 'hola'
###training full:
#data.generate_simple_kaggle_file(final_preds, 'bagged_{}'.format(n_bags))
model = xgb.train(params,
dtrain,
num_boost_round=num_boost_rounds,
evals=watchlist,
early_stopping_rounds=50)
cv_preds = model.predict(dtest) + cv_preds
#prepare for the next iteration
df_bag, bag_targets = delete_some_outliers(df_train, train_targets)
dtrain = xgb.DMatrix(df_bag.values, bag_targets)
print(i, df_bag.shape)
#params['seed'] = i
num_boost_rounds = 155
cv_preds = cv_preds / n_bags
mae = tools.get_mae_loss(test_targets, cv_preds)
mse = mean_squared_error(test_targets, cv_preds)
msg = 'mae: {}, mse: {}, train_data ratio: {}, bags:{}, r:{}'.format(
mae, mse, cv_split_ratio, n_bags, num_boost_rounds)
print(msg), logger.debug(msg)
else:
###training full:
df_train, targets, df_test = data.split_data(df, logerror)
dtest = xgb.DMatrix(df_test.values)
dtrain = xgb.DMatrix(df_train.values, targets)
params = model_params.get_xtune11k()
sub_preds = np.repeat(0, len(df_test))
num_boost_rounds = 110
df_train, targets, df_test = data.split_data(df, logerror)
new_train = tools.read_pickle(
cache_dir + 'ps_train_2ndx{}_f{}.pkl'.format(n_models, n_folds))
new_test = tools.read_pickle(
cache_dir + 'ps_test_2ndx{}_f{}.pkl'.format(n_models, n_folds))
new_train0 = tools.read_pickle(
cache_dir + 'ps_train_2ndx{}_f{}.pkl'.format(5, n_folds))
new_test0 = tools.read_pickle(cache_dir +
'ps_test_2ndx{}_f{}.pkl'.format(5, n_folds))
new_train['cat_weird'] = new_train['cat_preds'] + new_train['ker_preds']
new_test['cat_weird'] = new_test['cat_preds'] + new_test['ker_preds']
print 'score cat', tools.get_mae_loss(targets, new_train['cat_preds'])
print 'score xgb', tools.get_mae_loss(targets, new_train['xgb_preds'])
print 'score lgb', tools.get_mae_loss(targets, new_train['lgb_preds'])
print 'score keras', tools.get_mae_loss(targets, new_train['ker_preds'])
print 'score cat2', tools.get_mae_loss(targets, new_train['cat2_preds'])
print 'score cat3', tools.get_mae_loss(targets, new_train['cat3_preds'])
print 'score cat4', tools.get_mae_loss(targets, new_train['cat4_preds'])
print 'score cat weird', tools.get_mae_loss(targets,
new_train['cat_weird'])
train0 = np.zeros(len(new_train))
test0 = np.zeros(len(new_test))
#weirdness didnt work too well with 0.4, try 0.55
#weights = [0.34, 0.02, 0.06, 0.08, 0.02, 0.08, 0.42, .02] (best cv legal)
#weights = [.55, .02, .07, 0.09, 0.02] (weird submission)
if evaluate_cv:
df, targets, train_ixs, test_ixs = data.get_cv_ixs(df, targets)
else:
train_ixs, test_ixs = data.get_lb_ixs(targets)
df = features.add_features(df, train_ixs)
df = data.select_features(df)
print df.columns
df_train, train_targets = df.iloc[train_ixs], targets[train_ixs]
if evaluate_cv:
df_test, test_targets = df.iloc[test_ixs], targets[test_ixs]
eval_set = [(df_test.values, test_targets)]
else:
df_test = df.iloc[test_ixs]
eval_set = [(df_train.values, train_targets)]
params = model_params.get_ltune7k(num_rounds=nrounds)
model = LGBMRegressor(**params)
model.fit(df_train.values,
train_targets,
eval_set=eval_set,
early_stopping_rounds=80)
predictions = model.predict(df_test)
if evaluate_cv:
print(tools.get_mae_loss(test_targets, predictions))
if not evaluate_cv:
predictions = model.predict(df_test)
data.generate_simple_kaggle_file(predictions, 'sub_singlelgb_quasies')
df_test = df.iloc[test_ixs]
new_train = ss.io.read_pickle(
cache_dir + 'ps2_train_2ndx{}_f{}.pkl'.format(n_models, n_folds))
new_test = ss.io.read_pickle(
cache_dir + 'ps2_test_2ndx{}_f{}.pkl'.format(n_models, n_folds))
cols = new_train.columns
# new_train['cat_weird'] = new_train['cat_preds'] + new_train['ker_preds']
# new_test['cat_weird'] = new_test['cat_preds'] + new_test['ker_preds']
# new_train['zero'] = np.repeat(0, len(new_train))
# new_test['zero'] = np.repeat(0, len(new_test))
# cols = ['cat_weird', 'zero'] + list(cols)
for col in cols:
print 'score {}'.format(col), tools.get_mae_loss(
train_targets, new_train[col].values)
new_train = new_train[cols]
new_test = new_test[cols]
init_weights = np.repeat(0.1, n_models)
# init_weights = [0.1 0.1, 0.1, 0.1, 0.06, 0.08, 0.02, 0.08, 0.42] # (best cv legal)
all_train_preds = convert_preds_to_list(new_train)
optim = optimise_weights(all_train_preds,
train_targets,
init_weights,
minimise=True)
print "-", optim.fun
optimised_weights = optim.x
params = model_params.get_ctune163b()
print df.columns
if cv_flag:
df_full_train, targets, df_test = data.split_data(df, logerror)
df_train, df_test, train_targets, test_targets = data.split_cv(
df_full_train, targets, cv_split_ratio)
cv_preds = np.repeat(0, len(df_test))
for i in range(n_bags):
model = CatBoostRegressor(**params)
eval_set = [df_test.values, test_targets]
model.fit(df_train.values, train_targets, eval_set=eval_set)
predictions = model.predict(df_test)
mae = tools.get_mae_loss(test_targets, predictions)
mse = mean_squared_error(test_targets, predictions)
cv_preds = model.predict(df_test.values) + cv_preds
#prepare for the next iteration
#df_bag, bag_targets = delete_some_outliers(df_train, train_targets)
#print(i, df_bag.shape)
#params['seed'] = i
cv_preds = cv_preds / n_bags
mae = tools.get_mae_loss(test_targets, cv_preds)
mse = mean_squared_error(test_targets, cv_preds)
msg = 'mae: {}, mse: {}, train_data ratio: {}, bags:{}, r:{}'.format(
mae, mse, cv_split_ratio, n_bags, params['iterations'])
print(msg), logger.debug(msg)
exc = [
train.columns[c]
for c in range(len(train.columns)) if train.dtypes[c] == 'O'
] + ['logerror', 'parcelid']
col = [c for c in train.columns if c not in exc]
train = reg_features(train[col])
test[
'transactiondate'] = '2016-01-01' #should use the most common training date
test = reg_features(test[col])
reg = ElasticNetCV(normalize=True, l1_ratio=0.8, max_iter=5000)
reg.fit(train, y)
print('fit...')
print(tools.get_mae_loss(targets, reg.predict(train)))
########################
########################
## Combine and Save ##
########################
########################
##### COMBINE PREDICTIONS
print("\nCombining XGBoost, LightGBM, and baseline predicitons ...")
lgb_weight = (1 - XGB_WEIGHT - BASELINE_WEIGHT) / float((1 - OLS_WEIGHT))
xgb_weight0 = XGB_WEIGHT / (1 - OLS_WEIGHT)
baseline_weight0 = BASELINE_WEIGHT / (1 - OLS_WEIGHT)
pred0 = xgb_weight0 * xgb_pred + baseline_weight0 * BASELINE_PRED + lgb_weight * p_test
assert len(x_train_small) == len(x_train)
# keras
keras_ix = 2
batch_size, epochs = 256, 15
model = model_params.get_keras(x_train.shape[1])
history = model.fit(x_train,
y_train,
nb_epoch=epochs,
batch_size=batch_size,
validation_data=(x_val, y_val),
verbose=2)
model.history = history
preds_train[val_ix, keras_ix] = model.predict(x_val).squeeze()
preds_test[:, keras_ix] += model.predict(df_test.values).squeeze()
score = tools.get_mae_loss(y_val, preds_train[val_ix, keras_ix])
print('train rows:{}, val rows:{}, fold:{}, score:{}'.format(
len(x_train), len(x_val), i, score))
# svr !
svr_ix = 0
model = SVR(cache_size=600, C=0.1)
print x_train_small.shape
model.fit(x_train_small[::10, :], y_train[::10])
preds_train[val_ix, svr_ix] = model.predict(x_val_small)
preds_test[:, svr_ix] += model.predict(df_test_small.values)
score = tools.get_mae_loss(y_val, preds_train[val_ix, svr_ix])
print('train rows:{}, val rows:{}, fold:{}, score:{}'.format(
len(x_train), len(x_val), i, score))
# Catboost
