normal update, not many things

.vscode/settings.json

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+{
+    "python.analysis.extraPaths": [
+        "./codes",
+        "./regressors"
+    ]
+}

README.md

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 # ssa_everyone
 
-more test, more hope
+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。
+
+
+
+**test_full.py可以试试，就在这master位置打开把，要不然感觉可能会混乱位置**
+
+然后新regressor就往那边丢了，终于是分开了，天——
+
+细节可能需要修一修，average也有了，seed可选，model太大就不传勒
+
+
+
+明天考虑加入nn结构和其他更多厕所
+
+
+

codes/create_traindata.py

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+import pandas as pd
+from sklearn.model_selection import train_test_split
+from itertools import product
+import joblib
+
+
+def get_state_vect_cols(prefix=''):
+    if prefix:
+        prefix += '_'
+    vectors = ['r', 'v']
+    components = ['x', 'y', 'z']
+    col_names = [f'{prefix}{v}_{c}' for v, c in product(vectors, components)]
+    return col_names
+
+
+def create_train_data(seed = 0, test_size = 0.2):
+    """
+    Description
+    -----------
+    create a new train set from dataset(.parquet) by using seed
+
+    Parameters
+    ----------
+    seed : int (default=-1)
+        seed for train_test_split, let's say seed = 0 means random
+    
+    test_size : double (default=0.2)
+        test_size for 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")

regressors/linear_study.py

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+# wait for tommorow!

regressors/linear_sum_regressor.py

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+from normal_use import *
+
+sumRegressors = [LGBMRegressor, RandomForestRegressor, XGBRegressor, CatBoostRegressor]
+sumRegressor = Union[type(sumRegressors)]
+sumParams = [{},{},{},{"silent": True}]
+weight = [0.1, 0.2, 0.3, 0.4]
+Sums = {}
+train_test_data = None
+out_weights = []
+
+def get_random_small_train(X, y, Percentage = 0.8):
+    # return X_train & 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))

regressors/normal_use.py

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+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)

regressors/one_regressor.py

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+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)
+
+    

regressors/stacking_regressor.py

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+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))
+

test_full.py

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+# %%
+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)