Source code for rsmtool.modeler

"""
Class for training and predicting with built-in or SKLL models.

:author: Jeremy Biggs (jbiggs@ets.org)
:author: Anastassia Loukina (aloukina@ets.org)
:author: Nitin Madnani (nmadnani@ets.org)

:organization: ETS
"""

import logging
import pickle
from math import log10, sqrt
from os.path import join

import numpy as np
import pandas as pd
import statsmodels.api as sm
from numpy.random import RandomState
from scipy.optimize import nnls
from sklearn.linear_model import LassoCV
from skll.data import FeatureSet
from skll.learner import Learner

from .analyzer import Analyzer
from .container import DataContainer
from .preprocessor import FeaturePreprocessor
from .utils.metrics import compute_expected_scores_from_model
from .utils.models import is_skll_model
from .writer import DataWriter


[docs]class Modeler: """ Class to train model and generate predictions with built-in or SKLL models. """ def __init__(self, logger=None): """Instantiate empty instance with no learner and given logger, if any.""" self.learner = None self.logger = logger if logger else logging.getLogger(__name__)
[docs] @classmethod def load_from_file(cls, model_path): """ Load a modeler object from a file on disk. Parameters ---------- model_path : str The path to the modeler file. Returns ------- model : Modeler A Modeler instance. Raises ------ ValueError If ``model_path`` does not end with ".model". """ if not model_path.lower().endswith('.model'): raise ValueError('The file `{}` does not end with the ' 'proper extension. Please make sure that ' 'it is a `.model` file.'.format(model_path)) # Create SKLL learner from file learner = Learner.from_file(model_path) return cls.load_from_learner(learner)
[docs] @classmethod def load_from_learner(cls, learner): """ Create a new Modeler instance with a pre-populated learner. Parameters ---------- learner : skll.learner.Learner A SKLL Learner object. Returns ------- modeler : Modeler A Modeler object. Raises ------ TypeError If ``learner`` is not a SKLL Learner instance. """ if not isinstance(learner, Learner): raise TypeError('The `learner` argument must be a ' '` SKLL Learner` instance, not `{}`.' ''.format(type(learner))) # Create Modeler instance modeler = Modeler() modeler.learner = learner return modeler
[docs] @staticmethod def model_fit_to_dataframe(fit): """ Extract fit metrics from a ``statsmodels`` fit object into a data frame. Parameters ---------- fit : statsmodels.RegressionResults Model fit object obtained from a linear model trained using ``statsmodels.OLS``. Returns ------- df_fit : pandas DataFrame The output data frame with the main model fit metrics. """ df_fit = pd.DataFrame({"N responses": [int(fit.nobs)]}) df_fit['N features'] = int(fit.df_model) df_fit['R2'] = fit.rsquared df_fit['R2_adjusted'] = fit.rsquared_adj return df_fit
[docs] @staticmethod def ols_coefficients_to_dataframe(coefs): """ Convert series containing OLS coefficients to a data frame. Parameters ---------- coefs : pandas Series Series with feature names in the index and the coefficient values as the data, obtained from a linear model trained using ``statsmodels.OLS``. Returns ------- df_coef : pandas DataFrame Data frame with two columns: the feature name and the coefficient value. Note ---- The first row in the output data frame is always for the intercept and the rest are sorted by feature name. """ # first create a sorted data frame for all the non-intercept features non_intercept_columns = [c for c in coefs.index if c != 'const'] df_non_intercept = pd.DataFrame(coefs.filter(non_intercept_columns), columns=['coefficient']) df_non_intercept.index.name = 'feature' df_non_intercept = df_non_intercept.sort_index() df_non_intercept.reset_index(inplace=True) # now create a data frame that just has the intercept df_intercept = pd.DataFrame([{'feature': 'Intercept', 'coefficient': coefs['const']}]) # append the non-intercept frame to the intercept one df_coef = pd.concat([df_intercept, df_non_intercept], sort=True, ignore_index=True) # we always want to have the feature column first df_coef = df_coef[['feature', 'coefficient']] return df_coef
[docs] @staticmethod def skll_learner_params_to_dataframe(learner): """ Extract parameters from the given SKLL learner into a data frame. Parameters ---------- learner : skll.learner.Learner A SKLL learner object. Returns ------- df_coef : pandas DataFrame The data frame containing the model parameters from the given SKLL learner object. Note ---- 1. We use the ``coef_`` attribute of the scikit-learn model underlying the SKLL learner instead of the latter's ``model_params`` attribute. This is because ``model_params`` ignores zero coefficients, which we do not want. 2. The first row in the output data frame is always for the intercept and the rest are sorted by feature name. """ # get the intercept, coefficients, and feature names intercept = learner.model.intercept_ coefficients = learner.model.coef_ feature_names = learner.feat_vectorizer.get_feature_names() # first create a sorted data frame for all the non-intercept features df_non_intercept = pd.DataFrame({'feature': feature_names, 'coefficient': coefficients}) df_non_intercept = df_non_intercept.sort_values(by=['feature']) # now create a data frame that just has the intercept df_intercept = pd.DataFrame([{'feature': 'Intercept', 'coefficient': intercept}]) # append the non-intercept frame to the intercept one df_coef = pd.concat([df_intercept, df_non_intercept], sort=True, ignore_index=True) # we always want to have the feature column first df_coef = df_coef[['feature', 'coefficient']] return df_coef
[docs] def create_fake_skll_learner(self, df_coefficients): """ Create a fake SKLL linear regression learner from given coefficients. Parameters ---------- df_coefficients : pandas DataFrame The data frame containing the linear coefficients we want to create the fake SKLL model with. Returns ------- learner: skll.learner.Learner SKLL Learner object representing a ``LinearRegression`` model with the specified coefficients. """ # initialize a random number generator randgen = RandomState(1234567890) # iterate over the coefficients coefdict = {} for feature, coefficient in df_coefficients.itertuples(index=False): if feature == 'Intercept': intercept = coefficient else: # exclude NA coefficients if coefficient == np.nan: self.logger.warning("No coefficient was estimated for " "{}. This is likely due to exact " "collinearity in the model. This " "feature will not be used for model " "building".format(feature)) else: coefdict[feature] = coefficient learner = Learner('LinearRegression') num_features = len(coefdict) # excluding the intercept fake_feature_values = randgen.rand(num_features) fake_features = [dict(zip(coefdict, fake_feature_values))] fake_fs = FeatureSet('fake', ids=['1'], labels=[1.0], features=fake_features) learner.train(fake_fs, grid_search=False) # now create its parameters from the coefficients from the built-in model learner.model.coef_ = learner.feat_vectorizer.transform(coefdict).toarray()[0] learner.model.intercept_ = intercept return learner
[docs] def train_linear_regression(self, df_train, feature_columns): """ Train a "LinearRegression" model. This model is a simple linear regression model. Parameters ---------- df_train : pandas DataFrame Data frame containing the features on which to train the model. feature_columns : list A list of feature columns to use in training the model. Returns ------- learner : skll.learner.Learner The SKLL learner object. fit : statsmodels.RegressionResults A ``statsmodels`` regression results object. df_coef : pandas DataFrame Data frame containing the model coefficients. used_features : list of str A list of features used in the final model. """ # get the feature columns X = df_train[feature_columns] # add the intercept X = sm.add_constant(X) # fit the model fit = sm.OLS(df_train['sc1'], X).fit() df_coef = self.ols_coefficients_to_dataframe(fit.params) learner = self.create_fake_skll_learner(df_coef) # we used all the features used_features = feature_columns return learner, fit, df_coef, used_features
[docs] def train_equal_weights_lr(self, df_train, feature_columns): """ Train an "EqualWeightsLR" model. This model assigns the same weight to all features. Parameters ---------- df_train : pandas DataFrame Data frame containing the features on which to train the model. feature_columns : list of str A list of feature columns to use in training the model. Returns ------- learner : skll.learner.Learner The SKLL learner object. fit : statsmodels.RegressionResults A ``statsmodels`` regression results object. df_coef : pandas DataFrame Data frame containing the model coefficients. used_features : list of str A list of features used in the final model. """ # we first compute a single feature that is simply the sum of all features df_train_eqwt = df_train.copy() df_train_eqwt['sumfeature'] = df_train_eqwt[feature_columns].apply(np.sum, axis=1) # train a plain Linear Regression model X = df_train_eqwt['sumfeature'] X = sm.add_constant(X) fit = sm.OLS(df_train_eqwt['sc1'], X).fit() # get the coefficient for the summed feature and the intercept coef = fit.params['sumfeature'] const = fit.params['const'] # now we need to assign this coefficient to all of the original # features and create a fake SKLL learner with these weights original_features = [c for c in df_train_eqwt.columns if c not in ['sc1', 'sumfeature', 'spkitemid']] coefs = pd.Series(dict([(origf, coef) for origf in original_features] + [('const', const)])) df_coef = self.ols_coefficients_to_dataframe(coefs) # create fake SKLL learner with these coefficients learner = self.create_fake_skll_learner(df_coef) # we used all the features used_features = feature_columns return learner, fit, df_coef, used_features
[docs] def train_rebalanced_lr(self, df_train, feature_columns): """ Train a "RebalancedLR" model. This model balances empirical weights by changing betas (adapted from `here <https://stats.stackexchange.com/q/30876>`_). Parameters ---------- df_train : pandas DataFrame Data frame containing the features on which to train the model. feature_columns : list of str A list of feature columns to use in training the model. Returns ------- learner : skll.learner.Learner The SKLL learner object. fit : statsmodels.RegressionResults A ``statsmodels`` regression results object. df_coef : pandas DataFrame Data frame containing the model coefficients. used_features : list of str A list of features used in the final model. """ # train a plain Linear Regression model X = df_train[feature_columns] X = sm.add_constant(X) fit = sm.OLS(df_train['sc1'], X).fit() # convert the model parameters into a data frame df_params = self.ols_coefficients_to_dataframe(fit.params) df_params = df_params.set_index('feature') # compute the betas for the non-intercept coefficients df_weights = df_params.loc[feature_columns] df_betas = df_weights.copy() df_train_std = df_train[feature_columns].std() df_betas['coefficient'] = (df_weights['coefficient'].multiply(df_train_std, axis='index') / df_train['sc1'].std()) # replace each negative beta with delta and adjust # all the positive betas to account for this RT = 0.05 df_positive_betas = df_betas[df_betas['coefficient'] > 0] df_negative_betas = df_betas[df_betas['coefficient'] < 0] delta = np.sum(df_positive_betas['coefficient']) * RT / len(df_negative_betas) df_betas['coefficient'] = df_betas.apply(lambda row: row['coefficient'] * (1 - RT) if row['coefficient'] > 0 else delta, axis=1) # rescale the adjusted betas to get the new coefficients df_coef = df_betas['coefficient'] * df_train['sc1'].std() df_coef = df_coef.divide(df_train[feature_columns].std(), axis='index') # add the intercept back to the new coefficients df_coef['Intercept'] = df_params.loc['Intercept'].coefficient df_coef = df_coef.sort_index().reset_index() df_coef.columns = ['feature', 'coefficient'] # create fake SKLL learner with these coefficients learner = self.create_fake_skll_learner(df_coef) # we used all the features used_features = feature_columns return learner, fit, df_coef, used_features
[docs] def train_lasso_fixed_lambda_then_lr(self, df_train, feature_columns): """ Train a "LassoFixedLambdaThenLR" model. First do feature selection using lasso regression with a fixed lambda and then use only those features to train a second linear regression Parameters ---------- df_train : pandas DataFrame Data frame containing the features on which to train the model. feature_columns : list of str A list of feature columns to use in training the model. Returns ------- learner : skll.learner.Learner The SKLL learner object fit : statsmodels.RegressionResults A ``statsmodels`` regression results object. df_coef : pandas DataFrame The model coefficients in a data_frame used_features : list of str A list of features used in the final model. """ # train a Lasso Regression model with this featureset with a preset lambda p_lambda = sqrt(len(df_train) * log10(len(feature_columns))) # create a SKLL FeatureSet instance from the given data frame fs_train = FeatureSet.from_data_frame(df_train[feature_columns + ['sc1']], 'train', labels_column='sc1') # note that 'alpha' in sklearn is different from this lambda # so we need to normalize looking at the sklearn objective equation p_alpha = p_lambda / len(df_train) l_lasso = Learner('Lasso', model_kwargs={'alpha': p_alpha, 'positive': True}) l_lasso.train(fs_train, grid_search=False) # get the feature names that have the non-zero coefficients non_zero_features = list(l_lasso.model_params[0].keys()) # now train a new vanilla linear regression with just the non-zero features X = df_train[non_zero_features] X = sm.add_constant(X) fit = sm.OLS(df_train['sc1'], X).fit() # get the coefficients data frame df_coef = self.ols_coefficients_to_dataframe(fit.params) # create fake SKLL learner with these coefficients learner = self.create_fake_skll_learner(df_coef) # we used only the non-zero features used_features = non_zero_features return learner, fit, df_coef, used_features
[docs] def train_positive_lasso_cv_then_lr(self, df_train, feature_columns): """ Train a "PositiveLassoCVThenLR" model. First do feature selection using lasso regression optimized for log likelihood using cross validation and then use only those features to train a second linear regression. Parameters ---------- df_train : pandas DataFrame Data frame containing the features on which to train the model. feature_columns : list of str A list of feature columns to use in training the model. Returns ------- learner : skll.learner.Learner The SKLL learner object. fit : statsmodels.RegressionResults A statsmodels regression results object. df_coef : pandas DataFrame Data frame containing the model coefficients. used_features : list of str A list of features used in the final model. """ # train a LassoCV outside of SKLL since it's not exposed there X = df_train[feature_columns].values y = df_train['sc1'].values clf = LassoCV(cv=10, positive=True, random_state=1234567890) model = clf.fit(X, y) # get the non-zero features from this model non_zero_features = [] for feature, coefficient in zip(feature_columns, model.coef_): if coefficient != 0: non_zero_features.append(feature) # now train a new linear regression with just these non-zero features X = df_train[non_zero_features] X = sm.add_constant(X) fit = sm.OLS(df_train['sc1'], X).fit() # convert the model parameters into a data frame df_coef = self.ols_coefficients_to_dataframe(fit.params) # create fake SKLL learner with these coefficients learner = self.create_fake_skll_learner(df_coef) # we used only the non-zero features used_features = non_zero_features return learner, fit, df_coef, used_features
[docs] def train_non_negative_lr(self, df_train, feature_columns): """ Train an "NNLR" model. To do this, we first do feature selection using non-negative least squares (NNLS) and then use only its non-zero features to train another linear regression (LR) model. We do the regular LR at the end since we want an LR object so that we have access to R^2 and other useful statistics. There should be no difference between the non-zero coefficients from NNLS and the coefficients that end up coming out of the subsequent LR. Parameters ---------- df_train : pandas DataFrame Data frame containing the features on which to train the model. feature_columns : list of str A list of feature columns to use in training the model. Returns ------- learner : skll.learner.Learner The SKLL learner object. fit : statsmodels.RegressionResults A statsmodels regression results object. df_coef : pandas DataFrame Data frame containing the model coefficients. used_features : list of str A list of features used in the final model. """ # add an intercept to the features manually X = df_train[feature_columns].values intercepts = np.ones((len(df_train), 1)) X_plus_intercept = np.concatenate([intercepts, X], axis=1) y = df_train['sc1'].values # fit an NNLS model on this data coefs, rnorm = nnls(X_plus_intercept, y) # check whether the intercept is set to 0 and if so then we need # to flip the sign and refit the model to ensure that it is always # kept in the model if coefs[0] == 0: intercepts = -1 * np.ones((len(df_train), 1)) X_plus_intercept = np.concatenate([intercepts, X], axis=1) coefs, rnorm = nnls(X_plus_intercept, y) # separate the intercept and feature coefficients # intercept = coefs[0] coefficients = coefs[1:].tolist() # get the non-zero features from this model non_zero_features = [] for feature, coefficient in zip(feature_columns, coefficients): if coefficient != 0: non_zero_features.append(feature) # now train a new linear regression with just these non-zero features X = df_train[non_zero_features] X = sm.add_constant(X) fit = sm.OLS(df_train['sc1'], X).fit() # convert this model's parameters to a data frame df_coef = self.ols_coefficients_to_dataframe(fit.params) # create fake SKLL learner with these coefficients learner = self.create_fake_skll_learner(df_coef) # we used only the non-zero features used_features = non_zero_features return learner, fit, df_coef, used_features
[docs] def train_non_negative_lr_iterative(self, df_train, feature_columns): """ Train an "NNLR_iterative" model. For applications where there is a concern that standard NNLS may not converge, an alternate method of training NNLR by iteratively fitting OLS models, checking the coefficients, and dropping negative coefficients. First, fit an OLS model. Then, identify any variables whose coefficients are negative. Drop these variables from the model. Finally, refit the model. If any coefficients are still negative, set these to zero. Parameters ---------- df_train : pandas DataFrame Data frame containing the features on which to train the model. feature_columns : list of str A list of feature columns to use in training the model. Returns ------- learner : skll.learner.Learner The SKLL learner object. fit : statsmodels.RegressionResults A statsmodels regression results object. df_coef : pandas DataFrame Data frame containing the model coefficients. used_features : list of str A list of features used in the final model. """ X = df_train[feature_columns] X = sm.add_constant(X) y = df_train['sc1'] fit = sm.OLS(y, X).fit() positive_features = [] for name, value in fit.params.items(): if value >= 0 and name != 'const': positive_features.append(name) X = df_train[positive_features] X = sm.add_constant(X) fit = sm.OLS(y, X).fit() # if any parameters are still negative, set them to zero params = fit.params.copy() params = params.drop('const') if not (params >= 0).all(): fit.params[(fit.params < 0) & (fit.params.index != 'const')] = 0 # convert this model's parameters to a data frame df_coef = self.ols_coefficients_to_dataframe(fit.params) # create fake SKLL learner with these coefficients learner = self.create_fake_skll_learner(df_coef) # we used only the non-zero features used_features = positive_features return learner, fit, df_coef, used_features
[docs] def train_lasso_fixed_lambda_then_non_negative_lr(self, df_train, feature_columns): """ Train an "LassoFixedLambdaThenNNLR" model. First do feature selection using lasso regression and positive only weights. Then fit an NNLR (see above) on those features. Parameters ---------- df_train : pandas DataFrame Data frame containing the features on which to train the model. feature_columns : list of str A list of feature columns to use in training the model. Returns ------- learner : skll.learner.Learner The SKLL learner object. fit : statsmodels.RegressionResults A statsmodels regression results object. df_coef : pandas DataFrame Data frame containing the model coefficients. used_features : list of str A list of features used in the final model. """ # train a Lasso Regression model with a preset lambda p_lambda = sqrt(len(df_train) * log10(len(feature_columns))) # create a SKLL FeatureSet instance from the given data frame fs_train = FeatureSet.from_data_frame(df_train[feature_columns + ['sc1']], 'train', labels_column='sc1') # note that 'alpha' in sklearn is different from this lambda # so we need to normalize looking at the sklearn objective equation p_alpha = p_lambda / len(df_train) l_lasso = Learner('Lasso', model_kwargs={'alpha': p_alpha, 'positive': True}) l_lasso.train(fs_train, grid_search=False) # get the feature names that have the non-zero coefficients non_zero_features = list(l_lasso.model_params[0].keys()) # now train an NNLS regression using these non-zero features # first add an intercept to the features manually X = df_train[feature_columns].values intercepts = np.ones((len(df_train), 1)) X_plus_intercept = np.concatenate([intercepts, X], axis=1) y = df_train['sc1'].values # fit an NNLS model on this data coefs, rnorm = nnls(X_plus_intercept, y) # check whether the intercept is set to 0 and if so then we need # to flip the sign and refit the model to ensure that it is always # kept in the model if coefs[0] == 0: intercepts = -1 * np.ones((len(df_train), 1)) X_plus_intercept = np.concatenate([intercepts, X], axis=1) coefs, rnorm = nnls(X_plus_intercept, y) # separate the intercept and feature coefficients # even though we do not use intercept in the code # we define it here for readability # intercept = coefs[0] coefficients = coefs[1:].tolist() # get the non-zero features from this model non_zero_features = [] for feature, coefficient in zip(feature_columns, coefficients): if coefficient != 0: non_zero_features.append(feature) # now train a new linear regression with just these non-zero features X = df_train[non_zero_features] X = sm.add_constant(X) fit = sm.OLS(df_train['sc1'], X).fit() # convert this model's parameters into a data frame df_coef = self.ols_coefficients_to_dataframe(fit.params) # create fake SKLL learner with these coefficients learner = self.create_fake_skll_learner(df_coef) # we used only the positive features used_features = non_zero_features return learner, fit, df_coef, used_features
[docs] def train_lasso_fixed_lambda(self, df_train, feature_columns): """ Train a "LassoFixedLambda" model. This is a Lasso model with a fixed lambda. Parameters ---------- df_train : pandas DataFrame Data frame containing the features on which to train the model. feature_columns : list of str A list of feature columns to use in training the model. Returns ------- learner : skll.learner.Learner The SKLL learner object fit : ``None`` This is always ``None`` since there is no OLS model fitted in this case. df_coef : pandas DataFrame Data frame containing the model coefficients. used_features : list of str A list of features used in the final model. """ # train a Lasso Regression model with a preset lambda p_lambda = sqrt(len(df_train) * log10(len(feature_columns))) # create a SKLL FeatureSet instance from the given data frame fs_train = FeatureSet.from_data_frame(df_train[feature_columns + ['sc1']], 'train', labels_column='sc1') # note that 'alpha' in sklearn is different from this lambda # so we need to normalize looking at the sklearn objective equation alpha = p_lambda / len(df_train) learner = Learner('Lasso', model_kwargs={'alpha': alpha, 'positive': True}) learner.train(fs_train, grid_search=False) # convert this model's parameters to a data frame df_coef = self.skll_learner_params_to_dataframe(learner) # there's no OLS fit object in this case fit = None # we used all the features used_features = feature_columns return learner, fit, df_coef, used_features
[docs] def train_positive_lasso_cv(self, df_train, feature_columns): """ Train a "PositiveLassoCV" model. Do feature selection using lasso regression optimized for log likelihood using cross validation. All coefficients are constrained to have positive values. Parameters ---------- df_train : pandas DataFrame Data frame containing the features on which to train the model. feature_columns : list of str A list of feature columns to use in training the model. Returns ------- learner : skll.learner.Learner The SKLL learner object fit : ``None`` This is always ``None`` since there is no OLS model fitted in this case. df_coef : pandas DataFrame Data frame containing the model coefficients. used_features : list of str A list of features used in the final model. """ # train a LassoCV outside of SKLL since it's not exposed there X = df_train[feature_columns].values y = df_train['sc1'].values clf = LassoCV(cv=10, positive=True, random_state=1234567890) model = clf.fit(X, y) # save the non-zero model coefficients and intercept to a data frame non_zero_features, non_zero_feature_values = [], [] for feature, coefficient in zip(feature_columns, model.coef_): if coefficient != 0: non_zero_features.append(feature) non_zero_feature_values.append(coefficient) # initialize the coefficient data frame with just the intercept df_coef = pd.DataFrame([('Intercept', model.intercept_)]) df_coef = pd.concat([df_coef, pd.DataFrame(zip(non_zero_features, non_zero_feature_values))], ignore_index=True) df_coef.columns = ['feature', 'coefficient'] # create a fake SKLL learner with these non-zero weights learner = self.create_fake_skll_learner(df_coef) # there's no OLS fit object in this case fit = None # we used only the non-zero features used_features = non_zero_features return learner, fit, df_coef, used_features
[docs] def train_score_weighted_lr(self, df_train, feature_columns): """ Train a "ScoreWeightedLR" model. This is a linear regression model weighted by score. Parameters ---------- df_train : pandas DataFrame Data frame containing the features on which to train the model. feature_columns : list of str A list of feature columns to use in training the model. Returns ------- learner : skll.learner.Learner The SKLL learner object fit : statsmodels.RegressionResults A statsmodels regression results object. df_coef : pandas DataFrame Data frame containing the model coefficients. used_features : list of str A list of features used in the final model. """ # train weighted least squares regression # get the feature columns X = df_train[feature_columns] # add the intercept X = sm.add_constant(X) # define the weights as inverse proportion of total # number of data points for each score score_level_dict = df_train['sc1'].value_counts() expected_proportion = 1 / len(score_level_dict) score_weights_dict = {sc1: expected_proportion / count for sc1, count in score_level_dict.items()} weights = [score_weights_dict[sc1] for sc1 in df_train['sc1']] # fit the model fit = sm.WLS(df_train['sc1'], X, weights=weights).fit() df_coef = self.ols_coefficients_to_dataframe(fit.params) learner = self.create_fake_skll_learner(df_coef) # we used all the features used_features = feature_columns return learner, fit, df_coef, used_features
[docs] def train_builtin_model(self, model_name, df_train, experiment_id, filedir, figdir, file_format='csv'): """ Train one of the :ref:`built-in linear regression models <builtin_models>`. Parameters ---------- model_name : str Name of the built-in model to train. df_train : pandas DataFrame Data frame containing the features on which to train the model. The data frame must contain the ID column named "spkitemid" and the numeric label column named "sc1". experiment_id : str The experiment ID. filedir : str Path to the `output` experiment output directory. figdir : str Path to the `figure` experiment output directory. file_format : str, optional The format in which to save files. One of {"csv", "tsv", "xlsx"}. Defaults to "csv". Returns ------- learner : skll.learner.Learner SKLL ``LinearRegression`` `Learner <https://skll.readthedocs.io/en/latest/api/learner.html#skll.learner.Learner>`_ object containing the coefficients learned by training the built-in model. """ # get the columns that actually contain the feature values feature_columns = [c for c in df_train.columns if c not in ['spkitemid', 'sc1']] # LinearRegression if model_name == 'LinearRegression': result = self.train_linear_regression(df_train, feature_columns) # EqualWeightsLR elif model_name == 'EqualWeightsLR': result = self.train_equal_weights_lr(df_train, feature_columns) # RebalancedLR elif model_name == 'RebalancedLR': result = self.train_rebalanced_lr(df_train, feature_columns) # LassoFixedLambdaThenLR elif model_name == 'LassoFixedLambdaThenLR': result = self.train_lasso_fixed_lambda_then_lr(df_train, feature_columns) # PositiveLassoCVThenLR elif model_name == 'PositiveLassoCVThenLR': result = self.train_positive_lasso_cv_then_lr(df_train, feature_columns) # NNLR elif model_name == 'NNLR': result = self.train_non_negative_lr(df_train, feature_columns) # NNLRIterative elif model_name == 'NNLRIterative': result = self.train_non_negative_lr_iterative(df_train, feature_columns) # LassoFixedLambdaThenNNLR elif model_name == 'LassoFixedLambdaThenNNLR': result = self.train_lasso_fixed_lambda_then_non_negative_lr(df_train, feature_columns) # LassoFixedLambda elif model_name == 'LassoFixedLambda': result = self.train_lasso_fixed_lambda(df_train, feature_columns) # PositiveLassoCV elif model_name == 'PositiveLassoCV': result = self.train_positive_lasso_cv(df_train, feature_columns) # ScoreWeightedLR elif model_name == 'ScoreWeightedLR': result = self.train_score_weighted_lr(df_train, feature_columns) writer = DataWriter(experiment_id) frames = [] # unpack all results learner, fit, df_coef, used_features = result # add raw coefficients to frame list frames.append({'name': 'coefficients', 'frame': df_coef}) # compute the standardized and relative coefficients (betas) for the # non-intercept features and save to a file df_betas = df_coef.set_index('feature').loc[used_features] df_betas = df_betas.multiply(df_train[used_features].std(), axis='index') / df_train['sc1'].std() df_betas.columns = ['standardized'] df_betas['relative'] = df_betas / sum(abs(df_betas['standardized'])) df_betas.reset_index(inplace=True) # add betas to frame list frames.append({'name': 'betas', 'frame': df_betas}) # save the OLS fit object and its summary to files if fit: ols_file = join(filedir, '{}.ols'.format(experiment_id)) summary_file = join(filedir, '{}_ols_summary.txt'.format(experiment_id)) with open(ols_file, 'wb') as olsf, open(summary_file, 'w') as summf: pickle.dump(fit, olsf) summf.write(str(fit.summary())) # create a data frame with main model fit metrics and save to the file df_model_fit = self.model_fit_to_dataframe(fit) # add model_fit to frame list frames.append({'name': 'model_fit', 'frame': df_model_fit}) # save the SKLL model to a file model_file = join(filedir, '{}.model'.format(experiment_id)) learner.save(model_file) container = DataContainer(frames) writer.write_experiment_output(filedir, container, file_format=file_format) self.learner = learner return learner
[docs] def train_skll_model(self, model_name, df_train, experiment_id, filedir, figdir, file_format='csv', custom_fixed_parameters=None, custom_objective=None, predict_expected_scores=False): """ Train a SKLL classification or regression model. Parameters ---------- model_name : str Name of the SKLL model to train. df_train : pandas DataFrame Data frame containing the features on which to train the model. experiment_id : str The experiment ID. filedir : str Path to the "output" experiment output directory. figdir : str Path to the "figure" experiment output directory. file_format : str, optional The format in which to save files. For SKLL models, this argument does not actually change the format of the output files at this time, as no betas are computed. One of {"csv", "tsv", "xlsx"}. Defaults to "csv". custom_fixed_parameters : dict, optional A dictionary containing any fixed parameters for the SKLL model. Defaults to ``None``. custom_objective : str, optional Name of custom user-specified objective. If not specified or ``None``, "neg_mean_squared_error" is used as the objective. Defaults to ``None``. predict_expected_scores : bool, optional Whether we want the trained classifiers to predict expected scores. Defaults to ``False``. Returns ------- learner_and_objective : tuple A 2-tuple containing a SKLL Learner object of the appropriate type and the chosen tuning objective. """ # Instantiate the given SKLL learner and set its probability value # and fixed parameters appropriately model_kwargs = custom_fixed_parameters if custom_fixed_parameters is not None else {} learner = Learner(model_name, model_kwargs=model_kwargs, probability=predict_expected_scores) # get the features, IDs, and labels from the given data frame feature_columns = [c for c in df_train.columns if c not in ['spkitemid', 'sc1']] features = df_train[feature_columns].to_dict(orient='records') ids = df_train['spkitemid'].tolist() labels = df_train['sc1'].tolist() # create a FeatureSet and train the model fs = FeatureSet('train', ids=ids, labels=labels, features=features) # If we are training a SKLL regressor, then we want to use either the # user-specified objective or `neg_mean_squared_error`. If it's SKLL # classifier, then the choice is between the user-specified objective # and `f1_score_micro`. if learner.model_type._estimator_type == 'regressor': objective = 'neg_mean_squared_error' if not custom_objective else custom_objective else: objective = 'f1_score_micro' if not custom_objective else custom_objective learner.train(fs, grid_search=True, grid_objective=objective, grid_jobs=1) # TODO: compute betas for linear SKLL models? # save the SKLL model to disk with the given model name prefix model_file = join(filedir, '{}.model'.format(experiment_id)) learner.save(model_file) self.learner = learner # return the SKLL learner object and the chosen objective return learner, objective
[docs] def train(self, configuration, data_container, filedir, figdir, file_format='csv'): """ Train the given model on the given data and save the results. The main driver function to train the given model on the given data and save the results in the given directories using the given experiment ID as the prefix. parameters ---------- configuration : configuration_parser.Configuration A configuration object containing "experiment_id" and "model_name" parameters. data_container : container.DataContainer A data container object containing "train_preprocessed_features" data set. filedir : str Path to the "output" experiment output directory. figdir : str Path to the "figure" experiment output directory. file_format : str, optional The format in which to save files. One of {"csv", "tsv", "xlsx"}. Defaults to "csv". Returns ------- name : SKLL Learner object """ Analyzer.check_param_names(configuration, ['model_name', 'experiment_id']) Analyzer.check_frame_names(data_container, ['train_preprocessed_features']) model_name = configuration['model_name'] experiment_id = configuration['experiment_id'] df_train = data_container['train_preprocessed_features'] args = [model_name, df_train, experiment_id, filedir, figdir] kwargs = {'file_format': file_format} # add user-specified SKLL objective to the arguments if we are # training a SKLL model if is_skll_model(model_name): kwargs.update({'custom_fixed_parameters': configuration['skll_fixed_parameters'], 'custom_objective': configuration['skll_objective'], 'predict_expected_scores': configuration['predict_expected_scores']}) model, chosen_objective = self.train_skll_model(*args, **kwargs) configuration['skll_objective'] = chosen_objective else: model = self.train_builtin_model(*args, **kwargs) return model
[docs] def predict(self, df, min_score, max_score, predict_expected=False): """ Get raw predictions from given SKLL model on data in given data frame. Parameters ---------- df : pandas DataFrame Data frame containing features on which to make the predictions. The data must contain pre-processed feature values, an ID column named "spkitemid", and a label column named "sc1". min_score : int Minimum score level to be used if computing expected scores. max_score : int Maximum score level to be used if computing expected scores. predict_expected : bool, optional Predict expected scores for classifiers that return probability distributions over score. This will be ignored with a warning if the specified model does not support probability distributions. Note also that this assumes that the score range consists of contiguous integers - starting at ``min_score`` and ending at ``max_score``. Defaults to ``False``. Returns ------- df_predictions : pandas DataFrame Data frame containing the raw predictions, the IDs, and the human scores. Raises ------ ValueError If the model cannot predict probability distributions and ``predict_expected`` is set to ``True``. ValueError If the score range specified by ``min_score`` and ``max_score`` does not match what the model predicts in its probability distribution. """ model = self.learner feature_columns = [c for c in df.columns if c not in ['spkitemid', 'sc1']] features = df[feature_columns].to_dict(orient='records') ids = df['spkitemid'].tolist() # if we have the labels, save them in the featureset labels = None if 'sc1' in df: labels = df['sc1'].tolist() fs = FeatureSet('data', ids=ids, labels=labels, features=features) # if we are predicting expected scores, then call a different function predictions = compute_expected_scores_from_model(model, fs, min_score, max_score) if predict_expected else model.predict(fs) df_predictions = pd.DataFrame() df_predictions['spkitemid'] = ids df_predictions['raw'] = predictions # save the labels in the dataframe if they existed in the first place if labels: df_predictions['sc1'] = labels return df_predictions
[docs] def predict_train_and_test(self, df_train, df_test, configuration): """ Generate raw, scaled, and trimmed predictions on given data. Parameters ---------- df_train : pandas DataFrame Data frame containing the pre-processed training set features. df_test : pandas DataFrame Data frame containing the pre-processed test set features. configuration : configuration_parser.Configuration A configuration object containing "trim_max" and "trim_min" keys. Returns ------- List of data frames containing predictions and other information. """ Analyzer.check_param_names(configuration, ['trim_max', 'trim_min', 'trim_tolerance']) (trim_min, trim_max, trim_tolerance) = configuration.get_trim_min_max_tolerance() predict_expected_scores = configuration['predict_expected_scores'] df_train_predictions = self.predict(df_train, int(trim_min), int(trim_max), predict_expected=predict_expected_scores) df_test_predictions = self.predict(df_test, int(trim_min), int(trim_max), predict_expected=predict_expected_scores) # get the mean and SD of the training set predictions train_predictions_mean = df_train_predictions['raw'].mean() train_predictions_sd = df_train_predictions['raw'].std() # get the mean and SD of the human labels human_labels_mean = df_train['sc1'].mean() human_labels_sd = df_train['sc1'].std() self.logger.info('Processing train set predictions.') feature_preprocessor = FeaturePreprocessor() df_train_predictions = feature_preprocessor.process_predictions(df_train_predictions, train_predictions_mean, train_predictions_sd, human_labels_mean, human_labels_sd, trim_min, trim_max, trim_tolerance) self.logger.info('Processing test set predictions.') df_test_predictions = feature_preprocessor.process_predictions(df_test_predictions, train_predictions_mean, train_predictions_sd, human_labels_mean, human_labels_sd, trim_min, trim_max, trim_tolerance) df_postproc_params = pd.DataFrame([{'trim_min': trim_min, 'trim_max': trim_max, 'trim_tolerance': trim_tolerance, 'h1_mean': human_labels_mean, 'h1_sd': human_labels_sd, 'train_predictions_mean': train_predictions_mean, 'train_predictions_sd': train_predictions_sd}]) datasets = [{'name': 'pred_train', 'frame': df_train_predictions}, {'name': 'pred_test', 'frame': df_test_predictions}, {'name': 'postprocessing_params', 'frame': df_postproc_params}] # configuration options that are entirely for internal use internal_options_dict = {'train_predictions_mean': train_predictions_mean, 'train_predictions_sd': train_predictions_sd, 'human_labels_mean': human_labels_mean, 'human_labels_sd': human_labels_sd} new_configuration = configuration.copy() for key, value in internal_options_dict.items(): new_configuration[key] = value return new_configuration, DataContainer(datasets=datasets)
[docs] def get_feature_names(self): """ Get the feature names, if available. Returns ------- feature_names : list or ``None`` A list of feature names, or None if no learner was trained. """ if self.learner is not None: return self.learner.feat_vectorizer.get_feature_names_out().tolist() return None
[docs] def get_intercept(self): """ Get the intercept of the model, if available. Returns ------- intercept : float or ``None`` The intercept of the model. """ if self.learner is not None: return self.learner.model.intercept_ return None
[docs] def get_coefficients(self): """ Get the coefficients of the model, if available. Returns ------- coefficients : np.array or ``None`` The coefficients of the model, if available. """ if self.learner is not None: return self.learner.model.coef_ return None
[docs] def scale_coefficients(self, configuration): """ Scale coefficients using human scores & training set predictions. This procedure approximates what is done in operational setting but does not apply trimming to predictions. Parameters ---------- configuration : configuration_parser.Configuration A configuration object containing the "train_predictions_mean", "train_predictions_sd", and "human_labels_sd" parameters. Returns ------- data_container : container.DataContainer A container object containing the "coefficients_scaled" dataset. The frame for this dataset contains the scaled coefficients and the feature names, along with the intercept. Raises ------ RuntimeError If the model is non-linear and no coefficients are available. """ Analyzer.check_param_names(configuration, ['train_predictions_mean', 'train_predictions_sd', 'human_labels_sd']) train_predictions_mean = configuration['train_predictions_mean'] train_predictions_sd = configuration['train_predictions_sd'] h1_sd = configuration['human_labels_sd'] feature_names = self.get_feature_names() # try to get the model coefficients, if available try: coefficients = self.get_coefficients() except AttributeError: raise RuntimeError("no coefficients available for this model.") intercept = self.get_intercept() # scale the coefficients and the intercept scaled_coefficients = coefficients * h1_sd / train_predictions_sd # adjust the intercept to set the mean predicted score # to the mean of the training variable new_intercept = intercept * (h1_sd / train_predictions_sd) new_intercept += train_predictions_mean * (1 - h1_sd / train_predictions_sd) intercept_and_feature_names = ['Intercept'] + feature_names intercept_and_feature_values = [new_intercept] + list(scaled_coefficients) # create a data frame with new values df_scaled_coefficients = pd.DataFrame({'feature': intercept_and_feature_names, 'coefficient': intercept_and_feature_values}, columns=['feature', 'coefficient']) scaled_dataset = [{'name': 'coefficients_scaled', 'frame': df_scaled_coefficients}] return DataContainer(datasets=scaled_dataset)