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train.log
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2021-09-24 21:00:23,876 __main__ INFO START train.py
2021-09-24 21:00:23,877 __main__ INFO Load train df
2021-09-24 21:00:23,906 __main__ INFO Input shape: (2974, 76)
2021-09-24 21:00:23,921 __main__ ERROR Traceback (most recent call last):
File "/Users/elena.devyataykina/opt/anaconda3/envs/raifhack/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 2898, in get_loc
return self._engine.get_loc(casted_key)
File "pandas/_libs/index.pyx", line 70, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/index.pyx", line 101, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/hashtable_class_helper.pxi", line 1675, in pandas._libs.hashtable.PyObjectHashTable.get_item
File "pandas/_libs/hashtable_class_helper.pxi", line 1683, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: 'per_square_meter_price'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "train.py", line 46, in <module>
y_offer = train_df[train_df.price_type == PriceTypeEnum.OFFER_PRICE][TARGET]
File "/Users/elena.devyataykina/opt/anaconda3/envs/raifhack/lib/python3.8/site-packages/pandas/core/frame.py", line 2906, in __getitem__
indexer = self.columns.get_loc(key)
File "/Users/elena.devyataykina/opt/anaconda3/envs/raifhack/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 2900, in get_loc
raise KeyError(key) from err
KeyError: 'per_square_meter_price'
2021-09-24 21:03:56,777 __main__ INFO START train.py
2021-09-24 21:03:56,779 __main__ INFO Load train df
2021-09-24 21:03:56,808 __main__ INFO Input shape: (2974, 76)
2021-09-24 21:03:56,825 __main__ ERROR Traceback (most recent call last):
File "/Users/elena.devyataykina/opt/anaconda3/envs/raifhack/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 2898, in get_loc
return self._engine.get_loc(casted_key)
File "pandas/_libs/index.pyx", line 70, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/index.pyx", line 101, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/hashtable_class_helper.pxi", line 1675, in pandas._libs.hashtable.PyObjectHashTable.get_item
File "pandas/_libs/hashtable_class_helper.pxi", line 1683, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: 'per_square_meter_price'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "train.py", line 47, in <module>
y_offer = train_df[train_df.price_type == PriceTypeEnum.OFFER_PRICE][TARGET]
File "/Users/elena.devyataykina/opt/anaconda3/envs/raifhack/lib/python3.8/site-packages/pandas/core/frame.py", line 2906, in __getitem__
indexer = self.columns.get_loc(key)
File "/Users/elena.devyataykina/opt/anaconda3/envs/raifhack/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 2900, in get_loc
raise KeyError(key) from err
KeyError: 'per_square_meter_price'
2021-09-24 22:50:15,272 __main__ INFO START train.py
2021-09-24 22:50:15,273 __main__ INFO Load train df
2021-09-24 22:50:16,778 __main__ INFO Input shape: (279792, 77)
2021-09-24 22:50:17,185 __main__ INFO X_offer (275299, 70) y_offer (275299,) X_manual (4493, 70) y_manual (4493,)
2021-09-24 22:50:17,186 __main__ INFO Fit model
2021-09-24 22:50:17,186 raif_hack.model INFO Fit lightgbm
2021-09-24 22:51:26,805 raif_hack.model INFO Find corr coefficient
2021-09-24 22:51:27,391 raif_hack.model INFO Corr coef: -0.12
2021-09-24 22:51:27,391 __main__ INFO Save model
2021-09-24 22:52:03,838 __main__ INFO Metrics stat for training data with offers prices: {'mape': 1.5204271589249925, 'mdape': 0.3125603548666643, 'rmse': 73946.13496006273, 'r2': 0.8172816627975483, 'raif_metric': 3.4943808614617957}
2021-09-24 22:52:04,392 __main__ INFO Metrics stat for training data with manual prices: {'mape': 0.30544381035322893, 'mdape': 0.24759116446089766, 'rmse': 73135.9256925428, 'r2': 0.3308659827460806, 'raif_metric': 2.0449581821903826}
2021-09-24 22:52:04,392 __main__ INFO END train.py
2021-09-25 14:23:39,806 __main__ INFO START train.py
2021-09-25 14:23:39,807 __main__ INFO Load train df
2021-09-25 14:23:41,320 __main__ INFO Input shape: (279792, 77)
2021-09-25 14:24:32,017 __main__ INFO X_offer (275299, 75) y_offer (275299,) X_manual (4493, 75) y_manual (4493,)
2021-09-25 14:24:32,018 __main__ INFO Fit model
2021-09-25 14:24:32,018 raif_hack.model INFO Fit lightgbm
2021-09-25 14:24:32,035 __main__ ERROR Traceback (most recent call last):
File "train.py", line 67, in <module>
model.fit(X_offer, y_offer, X_manual, y_manual)
File "/Users/elena.devyataykina/PycharmProjects/raifhack/raif_hack/model.py", line 81, in fit
self.pipeline.fit(X_offer, y_offer, model__feature_name=[f'{i}' for i in range(70)],model__categorical_feature=['67','68','69'])
File "/Users/elena.devyataykina/opt/anaconda3/envs/raifhack/lib/python3.8/site-packages/sklearn/pipeline.py", line 341, in fit
Xt = self._fit(X, y, **fit_params_steps)
File "/Users/elena.devyataykina/opt/anaconda3/envs/raifhack/lib/python3.8/site-packages/sklearn/pipeline.py", line 303, in _fit
X, fitted_transformer = fit_transform_one_cached(
File "/Users/elena.devyataykina/opt/anaconda3/envs/raifhack/lib/python3.8/site-packages/joblib/memory.py", line 352, in __call__
return self.func(*args, **kwargs)
File "/Users/elena.devyataykina/opt/anaconda3/envs/raifhack/lib/python3.8/site-packages/sklearn/pipeline.py", line 754, in _fit_transform_one
res = transformer.fit_transform(X, y, **fit_params)
File "/Users/elena.devyataykina/opt/anaconda3/envs/raifhack/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py", line 505, in fit_transform
self._validate_remainder(X)
File "/Users/elena.devyataykina/opt/anaconda3/envs/raifhack/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py", line 332, in _validate_remainder
cols.extend(_get_column_indices(X, columns))
File "/Users/elena.devyataykina/opt/anaconda3/envs/raifhack/lib/python3.8/site-packages/sklearn/utils/__init__.py", line 398, in _get_column_indices
raise ValueError(f"Selected columns, {columns}, are not "
ValueError: Selected columns, ['region', 'city', 'realty_type', 'specific_floor', 'low_floor', 'basement', 'basement'], are not unique in dataframe
2021-09-25 14:24:52,616 __main__ INFO START train.py
2021-09-25 14:24:52,617 __main__ INFO Load train df
2021-09-25 14:24:54,159 __main__ INFO Input shape: (279792, 77)
2021-09-25 14:25:45,176 __main__ INFO X_offer (275299, 75) y_offer (275299,) X_manual (4493, 75) y_manual (4493,)
2021-09-25 14:25:45,177 __main__ INFO Fit model
2021-09-25 14:25:45,178 raif_hack.model INFO Fit lightgbm
2021-09-25 14:25:48,301 __main__ ERROR Traceback (most recent call last):
File "train.py", line 67, in <module>
model.fit(X_offer, y_offer, X_manual, y_manual)
File "/Users/elena.devyataykina/PycharmProjects/raifhack/raif_hack/model.py", line 81, in fit
self.pipeline.fit(X_offer, y_offer, model__feature_name=[f'{i}' for i in range(70)],model__categorical_feature=['67','68','69'])
File "/Users/elena.devyataykina/opt/anaconda3/envs/raifhack/lib/python3.8/site-packages/sklearn/pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "/Users/elena.devyataykina/opt/anaconda3/envs/raifhack/lib/python3.8/site-packages/lightgbm/sklearn.py", line 818, in fit
super().fit(X, y, sample_weight=sample_weight, init_score=init_score,
File "/Users/elena.devyataykina/opt/anaconda3/envs/raifhack/lib/python3.8/site-packages/lightgbm/sklearn.py", line 683, in fit
self._Booster = train(params, train_set,
File "/Users/elena.devyataykina/opt/anaconda3/envs/raifhack/lib/python3.8/site-packages/lightgbm/engine.py", line 228, in train
booster = Booster(params=params, train_set=train_set)
File "/Users/elena.devyataykina/opt/anaconda3/envs/raifhack/lib/python3.8/site-packages/lightgbm/basic.py", line 2229, in __init__
train_set.construct()
File "/Users/elena.devyataykina/opt/anaconda3/envs/raifhack/lib/python3.8/site-packages/lightgbm/basic.py", line 1468, in construct
self._lazy_init(self.data, label=self.label,
File "/Users/elena.devyataykina/opt/anaconda3/envs/raifhack/lib/python3.8/site-packages/lightgbm/basic.py", line 1298, in _lazy_init
return self.set_feature_name(feature_name)
File "/Users/elena.devyataykina/opt/anaconda3/envs/raifhack/lib/python3.8/site-packages/lightgbm/basic.py", line 1777, in set_feature_name
raise ValueError("Length of feature_name({}) and num_feature({}) don't match"
ValueError: Length of feature_name(70) and num_feature(75) don't match
2021-09-25 14:27:57,618 __main__ INFO START train.py
2021-09-25 14:27:57,618 __main__ INFO Load train df
2021-09-25 14:27:59,145 __main__ INFO Input shape: (279792, 77)
2021-09-25 14:28:49,633 __main__ INFO X_offer (275299, 75) y_offer (275299,) X_manual (4493, 75) y_manual (4493,)
2021-09-25 14:28:49,634 __main__ INFO Fit model
2021-09-25 14:28:49,634 raif_hack.model INFO Fit lightgbm
2021-09-25 14:29:55,711 raif_hack.model INFO Find corr coefficient
2021-09-25 14:29:56,403 raif_hack.model INFO Corr coef: -0.15
2021-09-25 14:29:56,403 __main__ INFO Save model
2021-09-25 14:30:38,397 __main__ INFO Metrics stat for training data with offers prices: {'mape': 1.5539720933623238, 'mdape': 0.31750534986807166, 'rmse': 73956.54720004869, 'r2': 0.8172302026063605, 'raif_metric': 3.5518317706420035}
2021-09-25 14:30:39,082 __main__ INFO Metrics stat for training data with manual prices: {'mape': 0.29947087204386524, 'mdape': 0.24171855439175338, 'rmse': 72864.75728809363, 'r2': 0.3358187230534131, 'raif_metric': 2.026473600059324}
2021-09-25 14:30:39,083 __main__ INFO END train.py
2021-09-25 14:32:25,669 __main__ INFO START predict.py
2021-09-25 14:32:25,670 __main__ INFO Load test df
2021-09-25 14:32:25,699 __main__ INFO Input shape: (2974, 76)
2021-09-25 14:32:26,265 __main__ INFO Load model
2021-09-25 14:32:26,294 __main__ INFO Predict
2021-09-25 14:32:26,357 __main__ INFO Save results
2021-09-25 14:32:26,369 __main__ INFO END predict.py
2021-09-25 14:39:19,853 __main__ INFO START train.py
2021-09-25 14:39:19,854 __main__ INFO Load train df
2021-09-25 14:39:21,452 __main__ INFO Input shape: (279792, 77)
2021-09-25 14:40:11,625 __main__ INFO X_offer (275299, 75) y_offer (275299,) X_manual (4493, 75) y_manual (4493,)
2021-09-25 14:40:11,626 __main__ INFO Fit model
2021-09-25 14:40:11,626 raif_hack.model INFO Fit lightgbm
2021-09-25 14:41:12,814 raif_hack.model INFO Find corr coefficient
2021-09-25 14:41:13,473 raif_hack.model INFO Corr coef: -0.15
2021-09-25 14:41:13,473 __main__ INFO Save model
2021-09-25 14:41:56,034 __main__ INFO Metrics stat for training data with offers prices: {'mape': 1.5539720933623238, 'mdape': 0.31750534986807166, 'rmse': 73956.54720004869, 'r2': 0.8172302026063605, 'raif_metric': 3.5518317706420035}
2021-09-25 14:41:56,707 __main__ INFO Metrics stat for training data with manual prices: {'mape': 0.29947087204386524, 'mdape': 0.24171855439175338, 'rmse': 72864.75728809363, 'r2': 0.3358187230534131, 'raif_metric': 2.026473600059324}
2021-09-25 14:41:56,707 __main__ INFO END train.py
2021-09-25 14:44:59,732 __main__ INFO START predict.py
2021-09-25 14:44:59,733 __main__ INFO Load test df
2021-09-25 14:44:59,762 __main__ INFO Input shape: (2974, 76)
2021-09-25 14:45:00,319 __main__ INFO Load model
2021-09-25 14:45:00,348 __main__ INFO Predict
2021-09-25 14:45:00,410 __main__ INFO Save results
2021-09-25 14:45:00,422 __main__ INFO END predict.py
2021-09-25 14:54:57,410 __main__ INFO START predict.py
2021-09-25 14:54:57,411 __main__ INFO Load test df
2021-09-25 14:54:57,448 __main__ INFO Input shape: (2974, 76)
2021-09-25 14:54:58,044 __main__ INFO Load model
2021-09-25 14:54:58,076 __main__ INFO Predict
2021-09-25 14:54:58,140 __main__ INFO Save results
2021-09-25 14:54:58,152 __main__ INFO END predict.py
2021-09-25 14:55:04,839 __main__ INFO START train.py
2021-09-25 14:55:04,839 __main__ INFO Load train df
2021-09-25 14:55:06,485 __main__ INFO Input shape: (279792, 77)
2021-09-25 14:56:00,853 __main__ INFO X_offer (275299, 75) y_offer (275299,) X_manual (4493, 75) y_manual (4493,)
2021-09-25 14:56:00,854 __main__ INFO Fit model
2021-09-25 14:56:00,854 raif_hack.model INFO Fit lightgbm
2021-09-25 14:57:01,671 raif_hack.model INFO Find corr coefficient
2021-09-25 14:57:02,322 raif_hack.model INFO Corr coef: -0.15
2021-09-25 14:57:02,322 __main__ INFO Save model
2021-09-25 14:57:43,943 __main__ INFO Metrics stat for training data with offers prices: {'mape': 1.5561693697009877, 'mdape': 0.31819964390063415, 'rmse': 73968.23760113996, 'r2': 0.8171724167526015, 'raif_metric': 3.55436775513693}
2021-09-25 14:57:44,587 __main__ INFO Metrics stat for training data with manual prices: {'mape': 0.30060711979054633, 'mdape': 0.24297194163208305, 'rmse': 73252.87310977632, 'r2': 0.3287243250129712, 'raif_metric': 2.0426418295580953}
2021-09-25 14:57:44,587 __main__ INFO END train.py
2021-09-25 14:58:33,142 __main__ INFO START train.py
2021-09-25 14:58:33,142 __main__ INFO Load train df
2021-09-25 14:58:35,087 __main__ INFO Input shape: (279792, 77)
2021-09-25 14:59:29,651 __main__ INFO X_offer (275299, 75) y_offer (275299,) X_manual (4493, 75) y_manual (4493,)
2021-09-25 14:59:29,652 __main__ INFO Fit model
2021-09-25 14:59:29,652 raif_hack.model INFO Fit lightgbm
2021-09-25 15:00:28,894 raif_hack.model INFO Find corr coefficient
2021-09-25 15:00:29,541 raif_hack.model INFO Corr coef: -0.15
2021-09-25 15:00:29,541 __main__ INFO Save model
2021-09-25 15:01:10,707 __main__ INFO Metrics stat for training data with offers prices: {'mape': 1.5539720933623238, 'mdape': 0.31750534986807166, 'rmse': 73956.54720004869, 'r2': 0.8172302026063605, 'raif_metric': 3.5518317706420035}
2021-09-25 15:01:11,399 __main__ INFO Metrics stat for training data with manual prices: {'mape': 0.29947087204386524, 'mdape': 0.24171855439175338, 'rmse': 72864.75728809363, 'r2': 0.3358187230534131, 'raif_metric': 2.026473600059324}
2021-09-25 15:01:11,399 __main__ INFO END train.py
2021-09-25 15:05:28,830 __main__ INFO START predict.py
2021-09-25 15:05:28,831 __main__ INFO Load test df
2021-09-25 15:05:28,863 __main__ INFO Input shape: (2974, 76)
2021-09-25 15:05:29,419 __main__ INFO Load model
2021-09-25 15:05:29,445 __main__ INFO Predict
2021-09-25 15:05:29,651 __main__ INFO Save results
2021-09-25 15:05:29,662 __main__ INFO END predict.py
2021-09-25 18:09:19,959 __main__ INFO START train.py
2021-09-25 18:09:19,961 __main__ INFO Load train df
2021-09-25 18:09:22,540 __main__ INFO Input shape: (223833, 78)
2021-09-25 18:10:54,808 __main__ INFO X_offer (220113, 76) y_offer (220113,) X_manual (3720, 76) y_manual (3720,)
2021-09-25 18:10:54,809 __main__ INFO Fit model
2021-09-25 18:10:54,809 raif_hack.model INFO Fit lightgbm
2021-09-25 18:13:40,222 raif_hack.model INFO Find corr coefficient
2021-09-25 18:13:41,509 raif_hack.model INFO Corr coef: -0.14
2021-09-25 18:13:41,509 __main__ INFO Save model
2021-09-25 18:15:11,443 __main__ INFO Metrics stat for training data with offers prices: {'mape': 1.5366134111021492, 'mdape': 0.3153666102905555, 'rmse': 72373.33979711535, 'r2': 0.8266768136763464, 'raif_metric': 3.5186935951870866}
2021-09-25 18:15:12,869 __main__ INFO Metrics stat for training data with manual prices: {'mape': 0.2952126571685423, 'mdape': 0.24468484536420004, 'rmse': 74725.7558181062, 'r2': 0.3012634618815174, 'raif_metric': 2.0356981823669544}
2021-09-25 18:15:12,869 __main__ INFO END train.py
2021-09-25 18:15:21,614 __main__ INFO START predict.py
2021-09-25 18:15:21,624 __main__ INFO Load test df
2021-09-25 18:15:22,615 __main__ INFO Input shape: (55959, 78)
2021-09-25 18:15:47,405 __main__ INFO Load model
2021-09-25 18:15:47,523 __main__ INFO Predict
2021-09-25 18:15:47,544 __main__ ERROR Traceback (most recent call last):
File "predict.py", line 60, in <module>
test_df['per_square_meter_price'] = model.predict(test_df[NUM_FEATURES+CATEGORICAL_OHE_FEATURES+CATEGORICAL_STE_FEATURES]) * 0.95
File "/home/yahabo/venvs/raif/lib/python3.8/site-packages/pandas/core/frame.py", line 2912, in __getitem__
indexer = self.loc._get_listlike_indexer(key, axis=1, raise_missing=True)[1]
File "/home/yahabo/venvs/raif/lib/python3.8/site-packages/pandas/core/indexing.py", line 1254, in _get_listlike_indexer
self._validate_read_indexer(keyarr, indexer, axis, raise_missing=raise_missing)
File "/home/yahabo/venvs/raif/lib/python3.8/site-packages/pandas/core/indexing.py", line 1304, in _validate_read_indexer
raise KeyError(f"{not_found} not in index")
KeyError: "['distance_from_city_center'] not in index"
2021-09-25 18:21:03,839 __main__ INFO START predict.py
2021-09-25 18:21:03,840 __main__ INFO Load test df
2021-09-25 18:21:04,545 __main__ INFO Input shape: (55959, 78)
2021-09-25 18:21:34,635 __main__ INFO Load model
2021-09-25 18:21:34,772 __main__ INFO Predict
2021-09-25 18:21:46,895 __main__ INFO Save results
2021-09-25 18:21:47,237 __main__ INFO END predict.py
2021-09-25 18:41:35,144 __main__ INFO START train.py
2021-09-25 18:41:35,146 __main__ INFO Load train df
2021-09-25 18:41:38,848 __main__ INFO Input shape: (223833, 78)
2021-09-25 18:44:07,971 __main__ INFO X_offer (220113, 76) y_offer (220113,) X_manual (3720, 76) y_manual (3720,)
2021-09-25 18:44:07,984 __main__ INFO Fit model
2021-09-25 18:44:07,985 raif_hack.model INFO Fit lightgbm
2021-09-25 18:46:47,450 raif_hack.model INFO Find corr coefficient
2021-09-25 18:46:48,415 raif_hack.model INFO Corr coef: -0.14
2021-09-25 18:46:48,416 __main__ INFO Save model
2021-09-25 18:47:50,524 __main__ INFO Metrics stat for training data with offers prices: {'mape': 1.534225323153888, 'mdape': 0.3153104006531624, 'rmse': 72276.94191597337, 'r2': 0.8271382228315561, 'raif_metric': 3.520571708830829}
2021-09-25 18:47:51,518 __main__ INFO Metrics stat for training data with manual prices: {'mape': 0.2969952210813923, 'mdape': 0.24569143032577126, 'rmse': 75104.46323598285, 'r2': 0.29416317253246815, 'raif_metric': 2.057295291528809}
2021-09-25 18:47:51,518 __main__ INFO END train.py
2021-09-25 18:47:58,232 __main__ INFO START predict.py
2021-09-25 18:47:58,234 __main__ INFO Load test df
2021-09-25 18:47:59,114 __main__ INFO Input shape: (55959, 78)
2021-09-25 18:48:35,191 __main__ INFO Load model
2021-09-25 18:48:35,279 __main__ INFO Predict
2021-09-25 18:48:44,944 __main__ INFO Save results
2021-09-25 18:48:45,137 __main__ INFO END predict.py
2021-09-25 18:51:21,395 __main__ INFO START predict.py
2021-09-25 18:51:21,396 __main__ INFO Load test df
2021-09-25 18:51:22,081 __main__ INFO Input shape: (55959, 78)
2021-09-25 18:51:56,670 __main__ INFO Load model
2021-09-25 18:51:56,764 __main__ INFO Predict
2021-09-25 18:52:06,706 __main__ INFO Save results
2021-09-25 18:52:06,915 __main__ INFO END predict.py
2021-09-25 18:55:54,902 __main__ INFO START train.py
2021-09-25 18:55:54,903 __main__ INFO Load train df
2021-09-25 18:55:58,526 __main__ INFO Input shape: (223833, 78)
2021-09-25 18:58:13,027 __main__ INFO X_offer (220113, 76) y_offer (220113,) X_manual (3720, 76) y_manual (3720,)
2021-09-25 18:58:13,027 __main__ INFO Fit model
2021-09-25 18:58:13,027 raif_hack.model INFO Fit lightgbm
2021-09-25 19:00:50,766 raif_hack.model INFO Find corr coefficient
2021-09-25 19:00:51,574 raif_hack.model INFO Corr coef: -0.14
2021-09-25 19:00:51,574 __main__ INFO Save model
2021-09-25 19:00:52,077 __main__ ERROR Traceback (most recent call last):
File "train.py", line 79, in <module>
"importance": model.feature_importances_
AttributeError: 'BenchmarkModel' object has no attribute 'feature_importances_'
2021-09-25 19:04:19,038 __main__ INFO START train.py
2021-09-25 19:04:19,067 __main__ INFO Load train df
2021-09-25 19:04:22,153 __main__ INFO Input shape: (223833, 78)
2021-09-25 19:06:24,145 __main__ INFO X_offer (220113, 76) y_offer (220113,) X_manual (3720, 76) y_manual (3720,)
2021-09-25 19:06:24,145 __main__ INFO Fit model
2021-09-25 19:06:24,145 raif_hack.model INFO Fit lightgbm
2021-09-25 19:08:50,979 raif_hack.model INFO Find corr coefficient
2021-09-25 19:08:51,802 raif_hack.model INFO Corr coef: -0.14
2021-09-25 19:08:51,802 __main__ INFO Save model
2021-09-25 19:09:45,733 __main__ INFO Metrics stat for training data with offers prices: {'mape': 1.534225323153888, 'mdape': 0.3153104006531624, 'rmse': 72276.94191597337, 'r2': 0.8271382228315561, 'raif_metric': 3.520571708830829}
2021-09-25 19:09:46,687 __main__ INFO Metrics stat for training data with manual prices: {'mape': 0.2969952210813923, 'mdape': 0.24569143032577126, 'rmse': 75104.46323598285, 'r2': 0.29416317253246815, 'raif_metric': 2.057295291528809}
2021-09-25 19:09:46,687 __main__ INFO END train.py
2021-09-25 19:28:10,868 __main__ INFO START train.py
2021-09-25 19:28:10,870 __main__ INFO Load train df
2021-09-25 19:28:14,283 __main__ INFO Input shape: (223833, 78)
2021-09-25 19:30:24,781 __main__ INFO X_offer (220113, 76) y_offer (220113,) X_manual (3720, 76) y_manual (3720,)
2021-09-25 19:30:24,855 __main__ INFO Fit model
2021-09-25 19:30:24,880 raif_hack.model INFO Fit lightgbm
2021-09-25 19:33:08,176 raif_hack.model INFO Find corr coefficient
2021-09-25 19:33:09,063 raif_hack.model INFO Corr coef: -0.15
2021-09-25 19:33:09,063 __main__ INFO Save model
2021-09-25 19:34:06,187 __main__ INFO Metrics stat for training data with offers prices: {'mape': 1.5336218230983676, 'mdape': 0.3146214379349961, 'rmse': 72366.96101867133, 'r2': 0.8267073647462873, 'raif_metric': 3.5176675710175704}
2021-09-25 19:34:07,151 __main__ INFO Metrics stat for training data with manual prices: {'mape': 0.2957796875579397, 'mdape': 0.24383337018098916, 'rmse': 75232.32195821941, 'r2': 0.291757877025666, 'raif_metric': 2.0377078122628647}
2021-09-25 19:34:07,151 __main__ INFO END train.py
2021-09-25 19:34:13,536 __main__ INFO START predict.py
2021-09-25 19:34:13,576 __main__ INFO Load test df
2021-09-25 19:34:14,511 __main__ INFO Input shape: (55959, 78)
2021-09-25 19:34:45,250 __main__ INFO Load model
2021-09-25 19:34:45,324 __main__ INFO Predict
2021-09-25 19:34:52,641 __main__ INFO Save results
2021-09-25 19:34:52,819 __main__ INFO END predict.py
2021-09-25 19:39:53,790 __main__ INFO START predict.py
2021-09-25 19:39:53,792 __main__ INFO Load test df
2021-09-25 19:39:54,436 __main__ INFO Input shape: (55959, 78)
2021-09-25 19:40:26,016 __main__ INFO Load model
2021-09-25 19:40:26,102 __main__ INFO Predict
2021-09-25 19:40:34,838 __main__ INFO Save results
2021-09-25 19:40:35,041 __main__ INFO END predict.py
2021-09-25 19:43:26,797 __main__ INFO START train.py
2021-09-25 19:43:26,799 __main__ INFO Load train df
2021-09-25 19:43:30,479 __main__ INFO Input shape: (223833, 78)
2021-09-25 19:44:44,226 __main__ INFO X_offer (220113, 75) y_offer (220113,) X_manual (3720, 75) y_manual (3720,)
2021-09-25 19:44:44,278 __main__ INFO Fit model
2021-09-25 19:44:44,278 raif_hack.model INFO Fit lightgbm
2021-09-25 19:47:25,036 raif_hack.model INFO Find corr coefficient
2021-09-25 19:47:26,329 raif_hack.model INFO Corr coef: -0.14
2021-09-25 19:47:26,330 __main__ INFO Save model
2021-09-25 19:48:26,675 __main__ INFO Metrics stat for training data with offers prices: {'mape': 1.532344550347006, 'mdape': 0.3153756179151173, 'rmse': 72429.8774115625, 'r2': 0.8264059098579106, 'raif_metric': 3.5227443934719602}
2021-09-25 19:48:27,662 __main__ INFO Metrics stat for training data with manual prices: {'mape': 0.29841397618200977, 'mdape': 0.24908089245905038, 'rmse': 75000.13158051291, 'r2': 0.2961228423604403, 'raif_metric': 2.067077989943281}
2021-09-25 19:48:27,662 __main__ INFO END train.py
2021-09-25 19:48:40,633 __main__ INFO START predict.py
2021-09-25 19:48:40,671 __main__ INFO Load test df
2021-09-25 19:48:41,478 __main__ INFO Input shape: (55959, 78)
2021-09-25 19:48:58,519 __main__ INFO Load model
2021-09-25 19:48:58,601 __main__ INFO Predict
2021-09-25 19:49:07,129 __main__ INFO Save results
2021-09-25 19:49:07,339 __main__ INFO END predict.py