# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in 

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory

from subprocess import check_output
print(check_output(["ls", "../input"]).decode("utf8"))


regressor_config_dict = {


    'sklearn.linear_model.ElasticNetCV': {
        'l1_ratio': np.arange(0.0, 1.01, 0.05),
        'tol': [1e-5, 1e-4, 1e-3, 1e-2, 1e-1]
    },

    'sklearn.ensemble.ExtraTreesRegressor': {
        'n_estimators': [100],
        'max_features': np.arange(0.05, 1.01, 0.05),
        'min_samples_split': range(2, 21),
        'min_samples_leaf': range(1, 21),
        'bootstrap': [True, False]
    },

    'sklearn.ensemble.GradientBoostingRegressor': {
        'n_estimators': [100],
        'loss': ["ls", "lad", "huber", "quantile"],
        'learning_rate': [1e-3, 1e-2, 1e-1, 0.5, 1.],
        'max_depth': range(1, 11),
        'min_samples_split': range(2, 21),
        'min_samples_leaf': range(1, 21),
        'subsample': np.arange(0.05, 1.01, 0.05),
        'max_features': np.arange(0.05, 1.01, 0.05),
        'alpha': [0.75, 0.8, 0.85, 0.9, 0.95, 0.99]
    },

    'sklearn.ensemble.AdaBoostRegressor': {
        'n_estimators': [100],
        'learning_rate': [1e-3, 1e-2, 1e-1, 0.5, 1.],
        'loss': ["linear", "square", "exponential"],
        'max_depth': range(1, 11)
    },

    'sklearn.tree.DecisionTreeRegressor': {
        'max_depth': range(1, 11),
        'min_samples_split': range(2, 21),
        'min_samples_leaf': range(1, 21)
    },

    'sklearn.neighbors.KNeighborsRegressor': {
        'n_neighbors': range(1, 101),
        'weights': ["uniform", "distance"],
        'p': [1, 2]
    },

    'sklearn.linear_model.LassoLarsCV': {
        'normalize': [True, False]
    },

    'sklearn.svm.LinearSVR': {
        'loss': ["epsilon_insensitive", "squared_epsilon_insensitive"],
        'dual': [True, False],
        'tol': [1e-5, 1e-4, 1e-3, 1e-2, 1e-1],
        'C': [1e-4, 1e-3, 1e-2, 1e-1, 0.5, 1., 5., 10., 15., 20., 25.],
        'epsilon': [1e-4, 1e-3, 1e-2, 1e-1, 1.]
    },

    'sklearn.ensemble.RandomForestRegressor': {
        'n_estimators': [100],
        'max_features': np.arange(0.05, 1.01, 0.05),
        'min_samples_split': range(2, 21),
        'min_samples_leaf': range(1, 21),
        'bootstrap': [True, False]
    },

    'sklearn.linear_model.RidgeCV': {
    },


    'xgboost.XGBRegressor': {
        'n_estimators': [100],
        'max_depth': range(1, 11),
        'learning_rate': [1e-3, 1e-2, 1e-1, 0.5, 1.],
        'subsample': np.arange(0.05, 1.01, 0.05),
        'min_child_weight': range(1, 21),
        'nthread': [1]
    },

    # Preprocesssors
    'sklearn.preprocessing.Binarizer': {
        'threshold': np.arange(0.0, 1.01, 0.05)
    },

    'sklearn.decomposition.FastICA': {
        'tol': np.arange(0.0, 1.01, 0.05)
    },

    'sklearn.cluster.FeatureAgglomeration': {
        'linkage': ['ward', 'complete', 'average'],
        'affinity': ['euclidean', 'l1', 'l2', 'manhattan', 'cosine', 'precomputed']
    },

    'sklearn.preprocessing.MaxAbsScaler': {
    },

    'sklearn.preprocessing.MinMaxScaler': {
    },

    'sklearn.preprocessing.Normalizer': {
        'norm': ['l1', 'l2', 'max']
    },

    'sklearn.kernel_approximation.Nystroem': {
        'kernel': ['rbf', 'cosine', 'chi2', 'laplacian', 'polynomial', 'poly', 'linear', 'additive_chi2', 'sigmoid'],
        'gamma': np.arange(0.0, 1.01, 0.05),
        'n_components': range(1, 11)
    },

    'sklearn.decomposition.PCA': {
        'svd_solver': ['randomized'],
        'iterated_power': range(1, 11)
    },

    'sklearn.preprocessing.PolynomialFeatures': {
        'degree': [2],
        'include_bias': [False],
        'interaction_only': [False]
    },

    'sklearn.kernel_approximation.RBFSampler': {
        'gamma': np.arange(0.0, 1.01, 0.05)
    },

    'sklearn.preprocessing.RobustScaler': {
    },

    'sklearn.preprocessing.StandardScaler': {
    },

    'tpot.builtins.ZeroCount': {
    },

    # Selectors
    'sklearn.feature_selection.SelectFwe': {
        'alpha': np.arange(0, 0.05, 0.001),
        'score_func': {
            'sklearn.feature_selection.f_classif': None
        }
    },

    'sklearn.feature_selection.SelectPercentile': {
        'percentile': range(1, 100),
        'score_func': {
            'sklearn.feature_selection.f_classif': None
        }
    },

    'sklearn.feature_selection.VarianceThreshold': {
        'threshold': np.arange(0.05, 1.01, 0.05)
    },

    'sklearn.feature_selection.SelectFromModel': {
        'threshold': np.arange(0, 1.01, 0.05),
        'estimator': {
            'sklearn.ensemble.ExtraTreesRegressor': {
                'n_estimators': [100],
                'max_features': np.arange(0.05, 1.01, 0.05)
            }
        }
    }

}