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101 lines
2.8 KiB
Python
101 lines
2.8 KiB
Python
# %%
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import pandas as pd
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# %%
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from itertools import product
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def get_state_vect_cols(prefix=''):
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if prefix:
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prefix += '_'
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vectors = ['r', 'v']
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components = ['x', 'y', 'z']
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col_names = [f'{prefix}{v}_{c}' for v, c in product(vectors, components)]
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return col_names
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# %%
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df = pd.read_parquet("traindata/physics_preds.parquet")
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test_set = df[df['aso_id'] == "05277"]
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train_set = df.groupby('aso_id').apply(lambda x: x.head(x.count()[0] - 3))
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print(df.count()[0], train_set.count()[0], test_set.count()[0])
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test_set
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# %%
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from sklearn.model_selection import train_test_split
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feature_cols = [
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'elapsed_seconds'
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] + get_state_vect_cols('physics_pred') + get_state_vect_cols('start')
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print(feature_cols)
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# The target values are the errors between the physical model predictions
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# and the ground truth observations
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target_cols = get_state_vect_cols('physics_err')
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print(target_cols)
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# Create feature and target matrices
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X = df[feature_cols]
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y = df[target_cols]
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data_keys = ['X_train', 'X_test', 'y_train', 'y_test']
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data_vals = train_test_split(X, y, test_size=0.2)
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train_test_data = dict(zip(data_keys, data_vals))
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# train_test_data['X_test'] = test_set[feature_cols]
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# train_test_data['y_test'] = test_set[target_cols]
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# train_test_data = {
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# 'X_train': train_set[feature_cols],
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# 'y_train': train_set[target_cols],
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# 'X_test': test_set[feature_cols],
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# 'y_test': test_set[target_cols],
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# }
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# %%
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from sklearn.utils.validation import check_X_y
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import pickle
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from catboost import CatBoostRegressor
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from lightgbm import LGBMRegressor
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from xgboost import XGBRegressor
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def train_model(regType):
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X, ys = train_test_data['X_train'], train_test_data['y_train']
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check_X_y(X, ys, multi_output=True)
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models = {}
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for target_col in ys.columns:
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y = ys[target_col]
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reg = regType()
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reg.fit(X, y, verbose=False)
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models[target_col] = reg
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print(target_col)
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CatBoostRegressor.__name__
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with open(f"models/model_{regType.__name__}.pickle", "wb") as f:
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pickle.dump(models, f)
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for reg in [CatBoostRegressor, LGBMRegressor, XGBRegressor]:
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train_model(reg)
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# %%
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from sklearn import metrics
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def eval_model(regType):
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with open(f"models/model_{regType.__name__}.pickle", "rb") as f:
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models = pickle.load(f)
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X, ys = train_test_data['X_test'], train_test_data['y_test']
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evals = []
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for target_col, reg in models.items():
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y_hat = reg.predict(X) # fake
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y = ys[target_col] # real
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rmse = metrics.mean_squared_error(y, y_hat, squared=False)
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r2 = metrics.r2_score(y, y_hat)
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eval_dict = {'Error': target_col, 'RMSE': rmse, 'R^2': r2}
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evals.append(eval_dict)
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print(regType.__name__)
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print(pd.DataFrame(evals))
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for reg in [CatBoostRegressor, LGBMRegressor, XGBRegressor]:
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eval_model(reg)
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# %%
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