From 386efc55232c12d63e77d69281a70561dc28b482 Mon Sep 17 00:00:00 2001 From: lj020 Date: Fri, 22 Oct 2021 20:48:09 +0800 Subject: [PATCH] normal update, not many things --- .vscode/settings.json | 6 + README.md | 21 +- catboost_info/catboost_training.json | 1004 +++++++++++++++++ catboost_info/learn/events.out.tfevents | Bin 0 -> 48870 bytes catboost_info/learn_error.tsv | 1001 ++++++++++++++++ catboost_info/time_left.tsv | 1001 ++++++++++++++++ .../create_traindata.cpython-38.pyc | Bin 0 -> 1776 bytes codes/create_traindata.py | 56 + create_datas/seed_114.td | Bin 0 -> 192049 bytes create_datas/seed_514.td | Bin 0 -> 192049 bytes .../linear_sum_regressor.cpython-38.pyc | Bin 0 -> 2712 bytes .../__pycache__/normal_use.cpython-38.pyc | Bin 0 -> 1006 bytes .../__pycache__/one_regressor.cpython-38.pyc | Bin 0 -> 1901 bytes .../stacking_regressor.cpython-38.pyc | Bin 0 -> 3253 bytes regressors/linear_study.py | 1 + regressors/linear_sum_regressor.py | 90 ++ regressors/normal_use.py | 23 + regressors/one_regressor.py | 64 ++ regressors/stacking_regressor.py | 119 ++ test_full.py | 27 + traindata/physics_preds.parquet | Bin 0 -> 209252 bytes traindata/usstratcom_data.parquet | Bin 0 -> 80822 bytes 22 files changed, 3412 insertions(+), 1 deletion(-) create mode 100644 .vscode/settings.json create mode 100644 catboost_info/catboost_training.json create mode 100644 catboost_info/learn/events.out.tfevents create mode 100644 catboost_info/learn_error.tsv create mode 100644 catboost_info/time_left.tsv create mode 100644 codes/__pycache__/create_traindata.cpython-38.pyc create mode 100644 codes/create_traindata.py create mode 100644 create_datas/seed_114.td create mode 100644 create_datas/seed_514.td create mode 100644 regressors/__pycache__/linear_sum_regressor.cpython-38.pyc create mode 100644 regressors/__pycache__/normal_use.cpython-38.pyc create mode 100644 regressors/__pycache__/one_regressor.cpython-38.pyc create mode 100644 regressors/__pycache__/stacking_regressor.cpython-38.pyc create mode 100644 regressors/linear_study.py create mode 100644 regressors/linear_sum_regressor.py create mode 100644 regressors/normal_use.py create mode 100644 regressors/one_regressor.py create mode 100644 regressors/stacking_regressor.py create mode 100644 test_full.py create mode 100644 traindata/physics_preds.parquet create mode 100644 traindata/usstratcom_data.parquet diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 0000000..b3c267d --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,6 @@ +{ + "python.analysis.extraPaths": [ + "./codes", + "./regressors" + ] +} \ No newline at end of file diff --git a/README.md b/README.md index b8a1a29..a64d048 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,22 @@ # ssa_everyone -more test, more hope \ No newline at end of file +more test, more hope + +目前更新的regressor技术都在同名文件夹，使用有点怪因为调整都在对应python里而不是总的test里。 + +明天在改把。 + + + 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train_test_split + + Returns + ------- + and traindata in folder "create_traindata" named "seed_{seed}.td" + + """ + + + df = pd.read_parquet("traindata/physics_preds.parquet") + feature_cols = [ + 'elapsed_seconds' + ] + get_state_vect_cols('physics_pred') + get_state_vect_cols('start') + print(feature_cols) + # The target values are the errors between the physical model predictions + # and the ground truth observations + target_cols = get_state_vect_cols('physics_err') + print(target_cols) + # Create feature and target matrices + X = df[feature_cols] + y = df[target_cols] + data_keys = ['X_train', 'X_test', 'y_train', 'y_test'] + if seed == 0: + data_vals = train_test_split(X, y, test_size=test_size) + else: + data_vals = train_test_split(X, y, test_size=test_size, random_state=seed) + train_test_data = dict(zip(data_keys, data_vals)) + + joblib.dump(train_test_data, f"create_datas/seed_{seed}.td") diff --git a/create_datas/seed_114.td b/create_datas/seed_114.td new file mode 100644 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y_train + data_keys = ['X_train', 'X_test', 'y_train', 'y_test'] + data_vals = train_test_split(X, y, test_size=(1-Percentage)) + train_test_data = dict(zip(data_keys, data_vals)) + return train_test_data['X_train'], train_test_data['y_train'] + + +def train_linear(id, regType: sumRegressor, use_RFsample = False): + X, ys = train_test_data['X_train'], train_test_data['y_train'] + if use_RFsample: + # X, ys = get_random_small_train(X, ys) + # which xxx_moon? + pass + check_X_y(X, ys, multi_output=True) + models = {} + for target_col in ys.columns: + y = ys[target_col] + reg = regType(**sumParams[id]) + reg.fit(X, y) + models[target_col] = reg + joblib.dump(models, f"linear/{regType.__name__}_{id}.model") + + +def eval_linear(id, regType: sumRegressor): + models = joblib.load(f"linear/{regType.__name__}_{id}.model") + X, ys = train_test_data['X_test'], train_test_data['y_test'] + evals = [] + out_w_dict = {'Regressor': regType.__name__, 'Weight': weight[id]} + out_weights.append(out_w_dict) + for target_col, reg in models.items(): + y_hat = reg.predict(X) # fake + y = ys[target_col] # real + rmse = metrics.mean_squared_error(y, y_hat, squared=False) + r2 = metrics.r2_score(y, y_hat) + eval_dict = {'Error': target_col, 'RMSE': rmse, 'R^2': r2} + evals.append(eval_dict) + if Sums.get(target_col) is None: + Sums[target_col] = weight[id] * y_hat + else: + Sums[target_col] += weight[id] * y_hat + print(f"{regType.__name__}_{id}") + print(pd.DataFrame(evals)) + + +def only_linear(trainset): + """ + Description + ----------- + create a linear combination, weight and regressors here to change + + Parameters + ---------- + trainset : dict + train_data_set + + Returns + ------- + print result on screen + """ + global train_test_data + train_test_data = trainset + + for i, reg in enumerate(sumRegressors): + train_linear(i, reg) + for i, reg in enumerate(sumRegressors): + eval_linear(i, reg) + ys = train_test_data['y_test'] + evals = [] + for target_col in Sums: + y_hat = Sums[target_col] # fake + y = ys[target_col] # real + rmse = metrics.mean_squared_error(y, y_hat, squared=False) + r2 = metrics.r2_score(y, y_hat) + eval_dict = {'Error': target_col, 'RMSE': rmse, 'R^2': r2} + evals.append(eval_dict) + print("linear sum for {} regressors!".format(len(sumRegressors))) + print(pd.DataFrame(out_weights)) + print(pd.DataFrame(evals)) + print("Average R2: ", average_R2(evals)) diff --git a/regressors/normal_use.py b/regressors/normal_use.py new file mode 100644 index 0000000..bc0a1f9 --- /dev/null +++ b/regressors/normal_use.py @@ -0,0 +1,23 @@ +from ngboost import NGBRegressor +from sklearn.ensemble import RandomForestRegressor +from catboost import CatBoostRegressor +from lightgbm import LGBMRegressor +from xgboost import XGBRegressor +from sklearn.linear_model import LogisticRegression +from sklearn.linear_model import LinearRegression +from sklearn.linear_model import Ridge +from sklearn.model_selection import KFold +import deepforest +import numpy as np +import pandas as pd +from typing import Union +from sklearn import metrics +from sklearn.model_selection import train_test_split +from sklearn.utils.validation import check_X_y +import joblib + +def average_R2(evals): + sum = 0 + for item in evals: + sum += item['R^2'] + return sum/len(evals) \ No newline at end of file diff --git a/regressors/one_regressor.py b/regressors/one_regressor.py new file mode 100644 index 0000000..fa13eeb --- /dev/null +++ b/regressors/one_regressor.py @@ -0,0 +1,64 @@ +from normal_use import * + + +Regressors = [NGBRegressor, RandomForestRegressor, CatBoostRegressor, LGBMRegressor, XGBRegressor] +Params = [{}, {}, {"silent": True},{},{}] +Regressor = Union[type(Regressors)] +train_test_data = None + + +def train_model(id, regType: Regressor): + X, ys = train_test_data['X_train'], train_test_data['y_train'] + check_X_y(X, ys, multi_output=True) + models = {} + for target_col in ys.columns: + y = ys[target_col] + reg = regType(**Params[id]) + reg.fit(X, y) + models[target_col] = reg + print(regType.__name__, target_col) + joblib.dump(models, f"models/{regType.__name__}.model") + + +def eval_model(regType: Regressor): + models = joblib.load(f"models/{regType.__name__}.model") + X, ys = train_test_data['X_test'], train_test_data['y_test'] + evals = [] + for target_col, reg in models.items(): + y_hat = reg.predict(X) # fake + y = ys[target_col] # real + rmse = metrics.mean_squared_error(y, y_hat, squared=False) + r2 = metrics.r2_score(y, y_hat) + eval_dict = {'Error': target_col, 'RMSE': rmse, 'R^2': r2} + evals.append(eval_dict) + print(regType.__name__) + print(pd.DataFrame(evals)) + print("Average R2: ", average_R2(evals)) + + +def train_one_models(trainsets): + """ + Description + ----------- + call this to start trainning each regressors. + + Parameters + ---------- + trainset : dict + use joblib to extract target dataset(create_datas) and put it in here. + + Returns + ------- + NO returns, but models in folder "models" and print R2 on screen + + """ + global train_test_data + train_test_data = trainsets + + for i, reg in enumerate(Regressors): + train_model(i, reg) + + for reg in Regressors: + eval_model(reg) + + \ No newline at end of file diff --git a/regressors/stacking_regressor.py b/regressors/stacking_regressor.py new file mode 100644 index 0000000..1e103f3 --- /dev/null +++ b/regressors/stacking_regressor.py @@ -0,0 +1,119 @@ +from normal_use import * + + +# -------------------------- here is stacking method -------------------------- +kf = KFold(n_splits=5, shuffle=True) +train_test_data = None +target_cols = None + +stacking_model_regressors = [CatBoostRegressor, LGBMRegressor, XGBRegressor, RandomForestRegressor] +stacking_model_params = [{"silent": True}, {}, {}, {}] + +# --------- change the stacking model here, use anything you want! --------- # +# LinearRegression, Ridge, XGBRegressor, CatBoostRegressor, LGBMRegressor +# deepforest.CascadeForestRegressor +REG_TOTAL = Ridge + + +class SklearnWrapper: + def __init__(self, clf, seed=0, params={}): + params['random_state'] = seed + self.clf = clf(**params) + + def train(self, x_train, y_train): + self.clf.fit(x_train, y_train) + + def predict(self, x): + return self.clf.predict(x) + + +def get_oof(clf, col_name): + x_train = train_test_data['X_train'] + y_train = train_test_data['y_train'][col_name] + x_test = train_test_data['X_test'] + + oof_train = np.zeros((x_train.shape[0],)) + oof_test = np.zeros((x_test.shape[0],)) + oof_test_skf = np.empty((5, x_test.shape[0])) + + for i, (train_index, valid_index) in enumerate(kf.split(x_train, y_train)): + trn_x, trn_y, val_x, val_y = x_train.iloc[train_index], y_train.iloc[ + train_index], x_train.iloc[valid_index], y_train.iloc[valid_index] + + clf.train(trn_x, trn_y) + oof_train[valid_index] = clf.predict(val_x) + oof_test_skf[i, :] = clf.predict(x_test) + + oof_test[:] = oof_test_skf.mean(axis=0) + return oof_train.reshape(-1, 1), oof_test.reshape(-1, 1) + + +def stack_model(train_stack, test_stack, y): + train_stack = np.hstack(train_stack) + test_stack = np.hstack(test_stack) + + oof = np.zeros((train_stack.shape[0],)) + + # usually we use this + # predictions = np.zeros((test_stack.shape[0],)) + # deepforest.CascadeForestRegressor needed below + predictions = np.zeros((test_stack.shape[0],1)) + + scores = [] + for fold_, (trn_idx, val_idx) in enumerate(kf.split(train_stack, y)): + trn_data, trn_y = train_stack[trn_idx], y.iloc[trn_idx] + val_data, val_y = train_stack[val_idx], y.iloc[val_idx] + + clf = REG_TOTAL() + clf.fit(trn_data, trn_y) + + tmp = clf.predict(test_stack) + tmp = tmp.reshape(-1,1) + predictions += tmp/5 + + return oof, predictions + + +def stacking_train(trainset): + """ + Description + ----------- + start stacking train + + Parameters + ---------- + trainset : dict + train_data_set + + Returns + ------- + print result + + """ + global target_cols, train_test_data + train_test_data = trainset + target_cols = train_test_data['y_train'].columns + + evals = [] + for col_name in target_cols: + oof_train = [] + oof_test = [] + y_train = train_test_data['y_train'][col_name] + for i, (reg, param) in enumerate(zip(stacking_model_regressors, stacking_model_params)): + regressor = SklearnWrapper(reg, params=param) + t_train, t_test = get_oof(regressor, col_name=col_name) + oof_train.append(t_train) + oof_test.append(t_test) + oof_stack, prediction_stack = stack_model(oof_train, oof_test, y_train) + + y_hat = prediction_stack # fake + y = train_test_data['y_test'][col_name] # real + rmse = metrics.mean_squared_error(y, y_hat, squared=False) + r2 = metrics.r2_score(y, y_hat) + eval_dict = {'Error': col_name, 'RMSE': rmse, 'R^2': r2} + evals.append(eval_dict) + print(f"{col_name} finished") + print(f"Stacking -- {REG_TOTAL.__name__}") + print(pd.DataFrame(evals)) + print("Average R2: ", average_R2(evals)) + diff --git a/test_full.py b/test_full.py new file mode 100644 index 0000000..ab8312e --- /dev/null +++ b/test_full.py @@ -0,0 +1,27 @@ +# %% +import joblib +import sys +sys.path.append("./codes") +sys.path.append("./regressors") +import create_traindata +import one_regressor +import stacking_regressor +import linear_sum_regressor + +# %% +# create train data +seed = 514 +create_traindata.create_train_data(seed=seed) +train_test_data = joblib.load(f"create_datas/seed_{seed}.td") + +# %% +# test one regressor +one_regressor.train_one_models(train_test_data) + +# %% +# test stacking method +stacking_regressor.stacking_train(train_test_data) + +# %% +# test linear combination +linear_sum_regressor.only_linear(train_test_data) \ No newline at end of file diff --git a/traindata/physics_preds.parquet b/traindata/physics_preds.parquet new file mode 100644 index 0000000000000000000000000000000000000000..8ced3e8bc324e4fdad48fe062596bd55fd571ce0 GIT binary patch literal 209252 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