Python sklearn.metrics.mean_squared_error() Examples
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Example #1
Source File: XGBoost_Regression_pm25.py From Machine-Learning-for-Beginner-by-Python3 with MIT License | 13 votes |
def Train(data, modelcount, censhu, yanzhgdata): model = xgb.XGBRegressor(max_depth=censhu, learning_rate=0.1, n_estimators=modelcount, silent=True, objective='reg:gamma') model.fit(data[:, :-1], data[:, -1]) # 给出训练数据的预测值 train_out = model.predict(data[:, :-1]) # 计算MSE train_mse = mse(data[:, -1], train_out) # 给出验证数据的预测值 add_yan = model.predict(yanzhgdata[:, :-1]) # 计算MSE add_mse = mse(yanzhgdata[:, -1], add_yan) print(train_mse, add_mse) return train_mse, add_mse # 最终确定组合的函数
Example #2
Source File: test.py From malss with MIT License | 7 votes |
def test_regression_small(): X, y = make_regression(n_samples=2000, n_features=10, n_informative=5, noise=30.0, random_state=0) X = pd.DataFrame(X) y = pd.Series(y) cls = MALSS('regression').fit(X, y, 'test_regression_small') cls.generate_module_sample() from sklearn.metrics import mean_squared_error pred = cls.predict(X) print(mean_squared_error(y, pred)) assert len(cls.algorithms) == 4 assert cls.algorithms[0].best_score is not None
Example #3
Source File: mean_squared_error.py From emmental with MIT License | 7 votes |
def mean_squared_error_scorer( golds: ndarray, probs: ndarray, preds: Optional[ndarray], uids: Optional[List[str]] = None, ) -> Dict[str, float]: """Mean squared error regression loss. Args: golds: Ground truth values. probs: Predicted probabilities. preds: Predicted values. uids: Unique ids, defaults to None. Returns: Mean squared error regression loss. """ return {"mean_squared_error": float(mean_squared_error(golds, probs))}
Example #4
Source File: LightGBM_Regression_pm25.py From Machine-Learning-for-Beginner-by-Python3 with MIT License | 7 votes |
def Train(data, modelcount, censhu, yanzhgdata): model = lgbm.LGBMRegressor(boosting_type='gbdt', objective='regression', num_leaves=1200, learning_rate=0.17, n_estimators=modelcount, max_depth=censhu, metric='rmse', bagging_fraction=0.8, feature_fraction=0.8, reg_lambda=0.9) model.fit(data[:, :-1], data[:, -1]) # 给出训练数据的预测值 train_out = model.predict(data[:, :-1]) # 计算MSE train_mse = mse(data[:, -1], train_out) # 给出验证数据的预测值 add_yan = model.predict(yanzhgdata[:, :-1]) # 计算MSE add_mse = mse(yanzhgdata[:, -1], add_yan) print(train_mse, add_mse) return train_mse, add_mse # 最终确定组合的函数
Example #5
Source File: test.py From malss with MIT License | 6 votes |
def test_regression_medium(): X, y = make_regression(n_samples=20000, n_features=10, n_informative=5, noise=30.0, random_state=0) X = pd.DataFrame(X) y = pd.Series(y) cls = MALSS('regression').fit(X, y, 'test_regression_medium') cls.generate_module_sample() from sklearn.metrics import mean_squared_error pred = cls.predict(X) print(mean_squared_error(y, pred)) assert len(cls.algorithms) == 2 assert cls.algorithms[0].best_score is not None
Example #6
Source File: test.py From malss with MIT License | 6 votes |
def test_regression_big(): X, y = make_regression(n_samples=200000, n_features=10, n_informative=5, noise=30.0, random_state=0) X = pd.DataFrame(X) y = pd.Series(y) cls = MALSS('regression').fit(X, y, 'test_regression_big') cls.generate_module_sample() from sklearn.metrics import mean_squared_error pred = cls.predict(X) print(mean_squared_error(y, pred)) assert len(cls.algorithms) == 1 assert cls.algorithms[0].best_score is not None
Example #7
Source File: model_eval.py From healthcareai-py with MIT License | 6 votes |
def calculate_regression_metrics(trained_sklearn_estimator, x_test, y_test): """ Given a trained estimator, calculate metrics. Args: trained_sklearn_estimator (sklearn.base.BaseEstimator): a scikit-learn estimator that has been `.fit()` y_test (numpy.ndarray): A 1d numpy array of the y_test set (predictions) x_test (numpy.ndarray): A 2d numpy array of the x_test set (features) Returns: dict: A dictionary of metrics objects """ # Get predictions predictions = trained_sklearn_estimator.predict(x_test) # Calculate individual metrics mean_squared_error = skmetrics.mean_squared_error(y_test, predictions) mean_absolute_error = skmetrics.mean_absolute_error(y_test, predictions) result = {'mean_squared_error': mean_squared_error, 'mean_absolute_error': mean_absolute_error} return result
Example #8
Source File: score_dataset.py From snape with Apache License 2.0 | 6 votes |
def score_regression(y, y_hat, report=True): """ Create regression score :param y: :param y_hat: :return: """ r2 = r2_score(y, y_hat) rmse = sqrt(mean_squared_error(y, y_hat)) mae = mean_absolute_error(y, y_hat) report_string = "---Regression Score--- \n" report_string += "R2 = " + str(r2) + "\n" report_string += "RMSE = " + str(rmse) + "\n" report_string += "MAE = " + str(mae) + "\n" if report: print(report_string) return mae, report_string
Example #9
Source File: pm25_RF_Regression.py From Machine-Learning-for-Beginner-by-Python3 with MIT License | 6 votes |
def Train(data, treecount, tezh, yanzhgdata): model = RF(n_estimators=treecount, max_features=tezh) model.fit(data[:, :-1], data[:, -1]) # 给出训练数据的预测值 train_out = model.predict(data[:, :-1]) # 计算MSE train_mse = mse(data[:, -1], train_out) # 给出验证数据的预测值 add_yan = model.predict(yanzhgdata[:, :-1]) # 计算MSE add_mse = mse(yanzhgdata[:, -1], add_yan) print(train_mse, add_mse) return train_mse, add_mse # 最终确定组合的函数
Example #10
Source File: AdaBoost_Regression.py From Machine-Learning-for-Beginner-by-Python3 with MIT License | 6 votes |
def Train(data, modelcount, censhu, yanzhgdata): model = AdaBoostRegressor(DecisionTreeRegressor(max_depth=censhu), n_estimators=modelcount, learning_rate=0.8) model.fit(data[:, :-1], data[:, -1]) # 给出训练数据的预测值 train_out = model.predict(data[:, :-1]) # 计算MSE train_mse = mse(data[:, -1], train_out) # 给出验证数据的预测值 add_yan = model.predict(yanzhgdata[:, :-1]) # 计算MSE add_mse = mse(yanzhgdata[:, -1], add_yan) print(train_mse, add_mse) return train_mse, add_mse # 最终确定组合的函数
Example #11
Source File: test_utils.py From gordo with GNU Affero General Public License v3.0 | 6 votes |
def test_metrics_wrapper(): # make the features in y be in different scales y = np.array([[1, 1], [2, 2], [3, 3], [4, 4], [5, 5]]) * [1, 100] # With no scaler provided it is relevant which of the two series gets an 80% error metric_func_noscaler = model_utils.metric_wrapper(mean_squared_error) mse_feature_one_wrong = metric_func_noscaler(y, y * [0.8, 1]) mse_feature_two_wrong = metric_func_noscaler(y, y * [1, 0.8]) assert not np.isclose(mse_feature_one_wrong, mse_feature_two_wrong) # With a scaler provided it is not relevant which of the two series gets an 80% # error scaler = MinMaxScaler().fit(y) metric_func_scaler = model_utils.metric_wrapper(mean_squared_error, scaler=scaler) mse_feature_one_wrong = metric_func_scaler(y, y * [0.8, 1]) mse_feature_two_wrong = metric_func_scaler(y, y * [1, 0.8]) assert np.isclose(mse_feature_one_wrong, mse_feature_two_wrong)
Example #12
Source File: test_builder.py From gordo with GNU Affero General Public License v3.0 | 6 votes |
def test_get_metrics_dict_scaler(scaler, mock): mock_model = mock metrics_list = [sklearn.metrics.mean_squared_error] # make the features in y be in different scales y = pd.DataFrame( np.array([[1, 1], [2, 2], [3, 3], [4, 4], [5, 5]]) * [1, 100], columns=["Tag 1", "Tag 2"], ) metrics_dict = ModelBuilder.build_metrics_dict(metrics_list, y, scaler=scaler) metric_func = metrics_dict["mean-squared-error"] mock_model.predict = lambda _y: _y * [0.8, 1] mse_feature_one_wrong = metric_func(mock_model, y, y) mock_model.predict = lambda _y: _y * [1, 0.8] mse_feature_two_wrong = metric_func(mock_model, y, y) if scaler: assert np.isclose(mse_feature_one_wrong, mse_feature_two_wrong) else: assert not np.isclose(mse_feature_one_wrong, mse_feature_two_wrong)
Example #13
Source File: test_builder.py From gordo with GNU Affero General Public License v3.0 | 6 votes |
def test_metrics_from_list(): """ Check getting functions from a list of metric names """ default = ModelBuilder.metrics_from_list() assert default == [ metrics.explained_variance_score, metrics.r2_score, metrics.mean_squared_error, metrics.mean_absolute_error, ] specifics = ModelBuilder.metrics_from_list( ["sklearn.metrics.adjusted_mutual_info_score", "sklearn.metrics.r2_score"] ) assert specifics == [metrics.adjusted_mutual_info_score, metrics.r2_score]
Example #14
Source File: test_averaging.py From nyaggle with MIT License | 6 votes |
def test_averaging_opt_minimize(): X, y = make_regression_df(n_samples=1024) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) oof, test = _make_1st_stage_preds(X_train, y_train, X_test) best_single_model = min(mean_squared_error(y_train, oof[0]), mean_squared_error(y_train, oof[1]), mean_squared_error(y_train, oof[2])) result = averaging_opt(test, oof, y_train, mean_squared_error, higher_is_better=False) assert result.score <= best_single_model result_simple_avg = averaging(test, oof, y_train, eval_func=mean_squared_error) assert result.score <= result_simple_avg.score
Example #15
Source File: test_run.py From nyaggle with MIT License | 6 votes |
def test_experiment_lgb_regressor(tmpdir_name): X, y = make_regression_df(n_samples=1024, n_num_features=10, n_cat_features=2, random_state=0, id_column='user_id') X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=0) params = { 'objective': 'regression', 'max_depth': 8 } result = run_experiment(params, X_train, y_train, X_test, tmpdir_name) assert len(np.unique(result.oof_prediction)) > 5 # making sure prediction is not binarized assert len(np.unique(result.test_prediction)) > 5 assert mean_squared_error(y_train, result.oof_prediction) == result.metrics[-1] _check_file_exists(tmpdir_name)
Example #16
Source File: test_tree.py From Mastering-Elasticsearch-7.0 with MIT License | 6 votes |
def test_boston(): # Check consistency on dataset boston house prices. for (name, Tree), criterion in product(REG_TREES.items(), REG_CRITERIONS): reg = Tree(criterion=criterion, random_state=0) reg.fit(boston.data, boston.target) score = mean_squared_error(boston.target, reg.predict(boston.data)) assert_less(score, 1, "Failed with {0}, criterion = {1} and score = {2}" "".format(name, criterion, score)) # using fewer features reduces the learning ability of this tree, # but reduces training time. reg = Tree(criterion=criterion, max_features=6, random_state=0) reg.fit(boston.data, boston.target) score = mean_squared_error(boston.target, reg.predict(boston.data)) assert_less(score, 2, "Failed with {0}, criterion = {1} and score = {2}" "".format(name, criterion, score))
Example #17
Source File: test_regression.py From Mastering-Elasticsearch-7.0 with MIT License | 6 votes |
def test_regression_metrics_at_limits(): assert_almost_equal(mean_squared_error([0.], [0.]), 0.00, 2) assert_almost_equal(mean_squared_log_error([0.], [0.]), 0.00, 2) assert_almost_equal(mean_absolute_error([0.], [0.]), 0.00, 2) assert_almost_equal(median_absolute_error([0.], [0.]), 0.00, 2) assert_almost_equal(max_error([0.], [0.]), 0.00, 2) assert_almost_equal(explained_variance_score([0.], [0.]), 1.00, 2) assert_almost_equal(r2_score([0., 1], [0., 1]), 1.00, 2) assert_raises_regex(ValueError, "Mean Squared Logarithmic Error cannot be " "used when targets contain negative values.", mean_squared_log_error, [-1.], [-1.]) assert_raises_regex(ValueError, "Mean Squared Logarithmic Error cannot be " "used when targets contain negative values.", mean_squared_log_error, [1., 2., 3.], [1., -2., 3.]) assert_raises_regex(ValueError, "Mean Squared Logarithmic Error cannot be " "used when targets contain negative values.", mean_squared_log_error, [1., -2., 3.], [1., 2., 3.])
Example #18
Source File: test_regression.py From Mastering-Elasticsearch-7.0 with MIT License | 6 votes |
def test_regression_custom_weights(): y_true = [[1, 2], [2.5, -1], [4.5, 3], [5, 7]] y_pred = [[1, 1], [2, -1], [5, 4], [5, 6.5]] msew = mean_squared_error(y_true, y_pred, multioutput=[0.4, 0.6]) maew = mean_absolute_error(y_true, y_pred, multioutput=[0.4, 0.6]) rw = r2_score(y_true, y_pred, multioutput=[0.4, 0.6]) evsw = explained_variance_score(y_true, y_pred, multioutput=[0.4, 0.6]) assert_almost_equal(msew, 0.39, decimal=2) assert_almost_equal(maew, 0.475, decimal=3) assert_almost_equal(rw, 0.94, decimal=2) assert_almost_equal(evsw, 0.94, decimal=2) # Handling msle separately as it does not accept negative inputs. y_true = np.array([[0.5, 1], [1, 2], [7, 6]]) y_pred = np.array([[0.5, 2], [1, 2.5], [8, 8]]) msle = mean_squared_log_error(y_true, y_pred, multioutput=[0.3, 0.7]) msle2 = mean_squared_error(np.log(1 + y_true), np.log(1 + y_pred), multioutput=[0.3, 0.7]) assert_almost_equal(msle, msle2, decimal=2)
Example #19
Source File: test_multioutput.py From Mastering-Elasticsearch-7.0 with MIT License | 6 votes |
def test_base_chain_crossval_fit_and_predict(): # Fit chain with cross_val_predict and verify predict # performance X, Y = generate_multilabel_dataset_with_correlations() for chain in [ClassifierChain(LogisticRegression()), RegressorChain(Ridge())]: chain.fit(X, Y) chain_cv = clone(chain).set_params(cv=3) chain_cv.fit(X, Y) Y_pred_cv = chain_cv.predict(X) Y_pred = chain.predict(X) assert Y_pred_cv.shape == Y_pred.shape assert not np.all(Y_pred == Y_pred_cv) if isinstance(chain, ClassifierChain): assert jaccard_score(Y, Y_pred_cv, average='samples') > .4 else: assert mean_squared_error(Y, Y_pred_cv) < .25
Example #20
Source File: domainAdaptation.py From dzetsaka with GNU General Public License v3.0 | 6 votes |
def __init__(self, transportAlgorithm="MappingTransport", scaler=False, params=None, feedback=True): try: from sklearn.metrics import mean_squared_error from itertools import product from sklearn.metrics import ( f1_score, cohen_kappa_score, accuracy_score) except BaseException: raise ImportError('Please install itertools and scikit-learn') self.transportAlgorithm = transportAlgorithm self.feedback = feedback self.params_ = params if scaler: from sklearn.preprocessing import MinMaxScaler self.scaler = MinMaxScaler(feature_range=(-1, 1)) self.scalerTarget = MinMaxScaler(feature_range=(-1, 1)) else: self.scaler = scaler
Example #21
Source File: regression_tests.py From drifter_ml with MIT License | 6 votes |
def mse_cv(self, cv): """ This method performs cross-validation over mean squared error. Parameters ---------- * cv : integer The number of cross validation folds to perform Returns ------- Returns a scores of the k-fold mean squared error. """ mse = metrics.make_scorer(metrics.mean_squared_error) result = cross_validate(self.reg, self.X, self.y, cv=cv, scoring=(mse)) return self.get_test_score(result)
Example #22
Source File: weather_forecasting2018_eval.py From Deep_Learning_Weather_Forecasting with Apache License 2.0 | 5 votes |
def rmse(a, b): return sqrt(mean_squared_error(a, b))
Example #23
Source File: eval_details_rmse.py From Deep_Learning_Weather_Forecasting with Apache License 2.0 | 5 votes |
def rmse(a, b): return sqrt(mean_squared_error(a, b))
Example #24
Source File: weather_forecasting2018_eval_my.py From Deep_Learning_Weather_Forecasting with Apache License 2.0 | 5 votes |
def rmse(a, b): return sqrt(mean_squared_error(a, b))
Example #25
Source File: eval_details_score.py From Deep_Learning_Weather_Forecasting with Apache License 2.0 | 5 votes |
def rmse(a, b): return sqrt(mean_squared_error(a, b))
Example #26
Source File: helper.py From Deep_Learning_Weather_Forecasting with Apache License 2.0 | 5 votes |
def rmse(y_pred, y_true): y_pred = y_pred.reshape(-1) y_true = y_true.reshape(-1) #print(y_true.shape) return np.sqrt(mean_squared_error(y_pred, y_true))
Example #27
Source File: regression.py From Kaggler with MIT License | 5 votes |
def rmse(y, p): """Root Mean Squared Error (RMSE). Args: y (numpy.array): target p (numpy.array): prediction Returns: e (numpy.float64): RMSE """ # check and get number of samples assert y.shape == p.shape return np.sqrt(mse(y, p))
Example #28
Source File: time_regression.py From astroalign with MIT License | 5 votes |
def plot(results, ax): df = results[["size", "time"]] df.plot.scatter(x='size', y='time', c='LightBlue', ax=ax, marker=".") # linear regression x = df["size"].values.reshape((-1, 1)) y = df["time"].values linear = LinearRegression().fit(x, y) y_pred = linear.predict(x) mqe = mean_squared_error(y, y_pred) r2 = r2_score(y, y_pred) ax.plot(x, y_pred, color='DarkBlue', linewidth=2) ax.set_title( "Linear regression between size and time " f"\n$mse={mqe:.3f}$ - $R^2={r2:.3f}$") ax.set_xlabel("Size") ax.set_ylabel("Seconds") return ax # ============================================================================= # CLI MAIN # =============================================================================
Example #29
Source File: metrics.py From tensorflow-XNN with MIT License | 5 votes |
def rmse(y_true, y_pred): assert y_true.shape == y_pred.shape return np.sqrt(mean_squared_error(y_true, y_pred))
Example #30
Source File: custom_metrics.py From sagemaker-xgboost-container with Apache License 2.0 | 5 votes |
def mse(preds, dtrain): """Compute mean squared error. For more information see: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html :param preds: Prediction values :param dtrain: Training data with labels :return: Metric name, mean squared error """ labels = dtrain.get_label() return 'mse', mean_squared_error(labels, preds)