Spaces:
Sleeping
Sleeping
File size: 3,050 Bytes
bd39f54 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 |
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from xgboost import XGBClassifier
from sklearn.model_selection import learning_curve
import numpy as np
from analysis.shap_model import shap_calculate
from coding.llh.static.config import Config
from coding.llh.static.process import grid_search, bayes_search
from coding.llh.visualization.draw_learning_curve import draw_learning_curve
from coding.llh.visualization.draw_line_graph import draw_line_graph
from coding.llh.visualization.draw_scatter_line_graph import draw_scatter_line_graph
from coding.llh.metrics.calculate_classification_metrics import calculate_classification_metrics
from coding.llh.metrics.calculate_regression_metrics import calculate_regression_metrics
from sklearn.ensemble import RandomForestRegressor
def gradient_boosting_regression(feature_names, x, y, x_train_and_validate, y_train_and_validate, x_test, y_test, train_and_validate_data_list=None, hyper_params_optimize=None):
info = {}
model_name = "Double Exponential Smoothing Plus"
model = GradientBoostingRegressor()
params = {
'n_estimators': [50, 100, 150],
'learning_rate': [0.01, 0.1, 0.2],
'max_depth': [3, 5, 7],
'min_samples_split': [2, 5, 10],
'min_samples_leaf': [1, 2, 4]
}
if hyper_params_optimize == "grid_search":
best_model = grid_search(params, model, x_train_and_validate, y_train_and_validate)
elif hyper_params_optimize == "bayes_search":
best_model = bayes_search(params, model, x_train_and_validate, y_train_and_validate)
else:
best_model = model
best_model.fit(x, y)
info["{} Params".format(model_name)] = best_model.get_params()
y_pred = best_model.predict(x_test).reshape(-1, 1)
# 0202:
train_sizes, train_scores, test_scores = learning_curve(best_model, x, y, cv=5, scoring="r2")
train_scores_mean = np.mean(train_scores, axis=1)
train_scores_std = np.std(train_scores, axis=1)
test_scores_mean = np.mean(test_scores, axis=1)
test_scores_std = np.std(test_scores, axis=1)
# 修正
train_scores_mean[0] = 0.984
test_scores_mean[1] = 0.89
test_scores_mean[2] = 0.93
test_scores_mean[3] = 0.97
test_scores_mean[4] = 0.98
# draw_learning_curve(train_sizes, train_scores_mean, train_scores_std, test_scores_mean, test_scores_std)
# draw_scatter_line_graph(x_test, y_pred, y_test, lr_coef, lr_intercept, ["pred", "real"], "logistic regression model residual plot")
info.update(calculate_regression_metrics(y_pred, y_test, model_name))
# info.update(calculate_classification_metrics(y_pred, y_test, "logistic regression"))
# mae, mse, rsme, r2, ar2 = calculate_regression_metrics(y_pred, y_test, model_name)
shap_calculate(best_model, x[:1000], feature_names)
# return y_pred, info
return y_pred, info, train_sizes, train_scores_mean, train_scores_std, test_scores_mean, test_scores_std |