Flattened jupyter notebooks containing the analysis workflow in the manuscript A quantitative model for the prediction of sooting tendency from molecular structure.
Full repository (with data files) is available at pstjohn/ysi_qsar_energy_fuels
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| name: ysi | |
| channels: | |
| - https://conda.anaconda.org/rdkit | |
| dependencies: | |
| - python=3.5 | |
| - jupyter | |
| - pandas | |
| - seaborn | |
| - rdkit | |
| - jinja2 | |
| - pip: | |
| - https://github.com/pstjohn/scikit-learn/archive/pstjohn-changes.zip | |
| - pubchempy | |
| - keras==0.3.3 |
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| import numpy as np | |
| np.random.seed(0) | |
| from keras.models import Sequential | |
| from keras.layers.core import Dense, Dropout, Activation | |
| from keras.wrappers.scikit_learn import KerasRegressor | |
| from sklearn.pipeline import Pipeline | |
| from sklearn.preprocessing import (StandardScaler, Normalizer, RobustScaler, | |
| MaxAbsScaler, Imputer) | |
| from sklearn.decomposition import PCA | |
| from sklearn.feature_selection import VarianceThreshold, RFE, SelectKBest, f_regression | |
| from sklearn import svm | |
| from ysi_utils.descriptors import dragon | |
| from ysi_utils.validation import y_train as y | |
| from ysi_utils.validation import y_test | |
| X = dragon.loc[y.index] | |
| X_test = dragon.loc[y_test.index] | |
| # These are the optimal parameters as selected by the hyperopt fitting routine. | |
| params = \ | |
| {'activation': 'relu', | |
| 'batch_size': 39, | |
| 'dropout1': 0.36632971139053216, | |
| 'dropout2': 0.030115015871571206, | |
| 'feature_extractor': {'n_features_to_select': 390, 'step': 10, 'type': 'RFE'}, | |
| 'nb_epochs': 100, | |
| 'num_layers': {'dropout3': 0.31435729897990794, | |
| 'layers': 'three', | |
| 'units3': 161}, | |
| 'optimizer': 'adam', | |
| 'preprocessor': 'MaxAbsScaler', | |
| 'units1': 140, | |
| 'units2': 140} | |
| def get_input_size(params): | |
| if 'n_components' in params['feature_extractor']: | |
| return params['feature_extractor']['n_components'] | |
| elif 'k' in params['feature_extractor']: | |
| return params['feature_extractor']['k'] | |
| elif 'n_features_to_select' in params['feature_extractor']: | |
| return params['feature_extractor']['n_features_to_select'] | |
| def build_model(params=params): | |
| model = Sequential() | |
| model.add(Dense(output_dim=params['units1'], | |
| input_dim=get_input_size(params))) | |
| model.add(Activation(params['activation'])) | |
| model.add(Dropout(params['dropout1'])) | |
| model.add(Dense(output_dim=params['units2'], init="glorot_uniform")) | |
| model.add(Activation(params['activation'])) | |
| model.add(Dropout(params['dropout2'])) | |
| if params['num_layers']['layers']== 'three': | |
| model.add(Dense(output_dim=params['num_layers']['units3'], init="glorot_uniform")) | |
| model.add(Activation(params['activation'])) | |
| model.add(Dropout(params['num_layers']['dropout3'])) | |
| model.add(Dense(output_dim=1)) | |
| model.compile(loss='mae', optimizer=params['optimizer']) | |
| return model | |
| ffann = KerasRegressor(build_fn=build_model, | |
| nb_epoch=params['nb_epochs'], | |
| batch_size=params['batch_size'], | |
| verbose=0) | |
| # Set up preprocessing pipeline | |
| imputer = Imputer() | |
| var_filter = VarianceThreshold() | |
| preprocessor_dict = { | |
| 'StandardScaler' : StandardScaler, | |
| 'MaxAbsScaler' : MaxAbsScaler, | |
| 'Normalizer' : Normalizer, | |
| 'RobustScaler' : RobustScaler, | |
| } | |
| scaler = preprocessor_dict[params['preprocessor']]() | |
| if params['feature_extractor']['type'] == 'pca': | |
| opts = dict(params['feature_extractor']) | |
| del opts['type'] | |
| feature_extraction = PCA(**opts) | |
| elif params['feature_extractor']['type'] == 'RFE': | |
| opts = dict(params['feature_extractor']) | |
| del opts['type'] | |
| svr = svm.SVR(kernel='linear') | |
| feature_extraction = RFE(estimator=svr, **opts) | |
| elif params['feature_extractor']['type'] == 'SelectKBest': | |
| opts = dict(params['feature_extractor']) | |
| del opts['type'] | |
| feature_extraction = SelectKBest(score_func=f_regression, **opts) | |
| model = Pipeline(steps=[ | |
| ('imputer', imputer), | |
| ('filter', var_filter), | |
| ('scaler', scaler), | |
| ('feature_extraction', feature_extraction), | |
| ('ffann', ffann) | |
| ]) |
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