def get_container(self, name_ext_tuples, converters=None): """ Get a DataContainer object from a list of tuples with (`name`, `ext`) """ names_ = [] paths_ = [] for name, ext in name_ext_tuples: if name == 'train': df = self.df_train elif name == 'test': df = self.df_test elif name == 'feature_specs': df = self.df_specs else: df = self.df_other path = TestDataReader.make_file_from_ext(df, ext) names_.append(name) paths_.append(path) reader = DataReader(paths_, names_, converters) container = reader.read() self.filepaths.extend(paths_) return container
def get_container(self, name_ext_tuples, converters=None): """ Get a DataContainer object from a list of tuples with (`name`, `ext`) """ names_ = [] paths_ = [] for name, ext in name_ext_tuples: if name == 'train': df = self.df_train elif name == 'test': df = self.df_test elif name == 'feature_specs': df = self.df_specs else: df = self.df_other path = TestDataReader.make_file_from_ext(df, ext) names_.append(name) paths_.append(path) reader = DataReader(paths_, names_, converters) container = reader.read() self.filepaths.extend(paths_) return container
def check_scaled_coefficients(source, experiment_id, file_format='csv'): """ Check that the predictions generated using scaled coefficients match the scaled scores. Raises an AssertionError if they do not. Parameters ---------- source : str Path to the source directory on disk. experiment_id : str The experiment ID. file_format : str, optional The format of the output files. Defaults to 'csv'. """ preprocessed_test_file = join('test_outputs', source, 'output', '{}_test_preprocessed_features.{}'.format(experiment_id, file_format)) scaled_coefficients_file = join('test_outputs', source, 'output', '{}_coefficients_scaled.{}'.format(experiment_id, file_format)) predictions_file = join('test_outputs', source, 'output', '{}_pred_processed.{}'.format(experiment_id, file_format)) postprocessing_params_file = join('test_outputs', source, 'output', '{}_postprocessing_params.{}'.format(experiment_id, file_format)) postproc_params = DataReader.read_from_file(postprocessing_params_file).loc[0] df_preprocessed_test_data = DataReader.read_from_file(preprocessed_test_file) df_old_predictions = DataReader.read_from_file(predictions_file) df_old_predictions = df_old_predictions[['spkitemid', 'sc1', 'scale']] # create fake skll objects with new coefficients df_coef = DataReader.read_from_file(scaled_coefficients_file) learner = Modeler.create_fake_skll_learner(df_coef) modeler = Modeler.load_from_learner(learner) # generate new predictions and rename the prediction column to 'scale' df_new_predictions = modeler.predict(df_preprocessed_test_data, postproc_params['trim_min'], postproc_params['trim_max']) df_new_predictions.rename(columns={'raw': 'scale'}, inplace=True) # check that new predictions match the scaled old predictions assert_frame_equal(df_new_predictions.sort_index(axis=1), df_old_predictions.sort_index(axis=1), check_exact=False, check_less_precise=True)
def check_subgroup_outputs(output_dir, experiment_id, subgroups, file_format='csv'): """ Check to make sure that the subgroup outputs look okay. Raise an AssertionError if they do not. Parameters ---------- output_dir : str Path to the `output` experiment output directory for a test. experiment_id : str The experiment ID. subgroups : list of str List of column names that contain grouping information. file_format : str, optional The format of the output files. Defaults to 'csv'. """ train_preprocessed_file = join(output_dir, '{}_train_metadata.{}'.format(experiment_id, file_format)) train_preprocessed = DataReader.read_from_file(train_preprocessed_file, index_col=0) test_preprocessed_file = join(output_dir, '{}_test_metadata.{}'.format(experiment_id, file_format)) test_preprocessed = DataReader.read_from_file(test_preprocessed_file, index_col=0) for group in subgroups: ok_(group in train_preprocessed.columns) ok_(group in test_preprocessed.columns) # check that the total sum of N per category matches the total N # in data composition and the total N categories matches what is # in overall data composition file_data_composition_all = join(output_dir, '{}_data_composition.{}'.format(experiment_id, file_format)) df_data_composition_all = DataReader.read_from_file(file_data_composition_all) for group in subgroups: file_composition_by_group = join(output_dir, '{}_data_composition_by_{}.{}'.format(experiment_id, group, file_format)) composition_by_group = DataReader.read_from_file(file_composition_by_group) for partition in ['Training', 'Evaluation']: partition_info = df_data_composition_all.loc[df_data_composition_all['partition'] == partition] summation = sum(composition_by_group['{} set' ''.format(partition)]) ok_(summation == partition_info.iloc[0]['responses']) length = len(composition_by_group.loc[composition_by_group['{} set' ''.format(partition)] != 0]) ok_(length == partition_info.iloc[0][group])
def check_scaled_coefficients(source, experiment_id, file_format='csv'): """ Check that the predictions generated using scaled coefficients match the scaled scores. Raises an AssertionError if they do not. Parameters ---------- source : str Path to the source directory on disk. experiment_id : str The experiment ID. file_format : str, optional The format of the output files. Defaults to 'csv'. """ preprocessed_test_file = join( 'test_outputs', source, 'output', '{}_test_preprocessed_features.{}'.format(experiment_id, file_format)) scaled_coefficients_file = join( 'test_outputs', source, 'output', '{}_coefficients_scaled.{}'.format(experiment_id, file_format)) predictions_file = join( 'test_outputs', source, 'output', '{}_pred_processed.{}'.format(experiment_id, file_format)) postprocessing_params_file = join( 'test_outputs', source, 'output', '{}_postprocessing_params.{}'.format(experiment_id, file_format)) postproc_params = DataReader.read_from_file( postprocessing_params_file).loc[0] df_preprocessed_test_data = DataReader.read_from_file( preprocessed_test_file) df_old_predictions = DataReader.read_from_file(predictions_file) df_old_predictions = df_old_predictions[['spkitemid', 'sc1', 'scale']] # create fake skll objects with new coefficients df_coef = DataReader.read_from_file(scaled_coefficients_file) learner = Modeler.create_fake_skll_learner(df_coef) modeler = Modeler.load_from_learner(learner) # generate new predictions and rename the prediction column to 'scale' df_new_predictions = modeler.predict(df_preprocessed_test_data, postproc_params['trim_min'], postproc_params['trim_max']) df_new_predictions.rename(columns={'raw': 'scale'}, inplace=True) # check that new predictions match the scaled old predictions assert_frame_equal(df_new_predictions.sort_index(axis=1), df_old_predictions.sort_index(axis=1), check_exact=False, check_less_precise=True)
def test_locate_files_list(self): paths = ['file1.csv', 'file2.xlsx'] config_dir = 'output' result = DataReader.locate_files(paths, config_dir) assert isinstance(result, list) eq_(result, [None, None])
def locate_custom_sections(custom_report_section_paths, config_dir): """ Get the absolute paths for custom report sections and check that the files exist. If a file does not exist, raise an exception. Parameters ---------- custom_report_section_paths : list of str List of paths to IPython notebook files representing the custom sections. config_dir : str Path to the experiment configuration file. Returns ------- custom_report_sections : list of str List of absolute paths to the custom section notebooks. Raises ------ FileNotFoundError If any of the files cannot be found. """ custom_report_sections = [] for cs_path in custom_report_section_paths: cs_location = DataReader.locate_files(cs_path, config_dir) if not cs_location: raise FileNotFoundError("Error: custom section not found at " "{}.".format(cs_path)) else: custom_report_sections.append(cs_location) return custom_report_sections
def test_locate_files_list(self): paths = ['file1.csv', 'file2.xlsx'] config_dir = 'output' result = DataReader.locate_files(paths, config_dir) assert isinstance(result, list) eq_(result, [None, None])
def check_experiment_dir(experiment_dir, experiment_name, configpath): """ Check that the supplied experiment directory exists and contains the output of the rsmtool experiment. Parameters ---------- experiment_dir : str Supplied path to the experiment_dir. configpath : str Path to the directory containing the configuration file. Returns ------- jsons : list A list paths to all configuration json files contained in the output directory Raises ------ FileNotFoundError If the directory does not exist or does not contain and output of an RSMTool experiment. """ full_path_experiment_dir = DataReader.locate_files(experiment_dir, configpath) if not full_path_experiment_dir: raise FileNotFoundError("The directory {} " "does not exist.".format(experiment_dir)) else: # check that there is an output directory csvdir = normpath(join(full_path_experiment_dir, 'output')) if not exists(csvdir): raise FileNotFoundError("The directory {} does not contain " "the output of an rsmtool " "experiment.".format(full_path_experiment_dir)) # find the json configuration files for all experiments stored in this directory jsons = glob.glob(join(csvdir, '*.json')) if len(jsons) == 0: raise FileNotFoundError("The directory {} does not contain " "the .json configuration files for rsmtool " "experiments.".format(full_path_experiment_dir)) # Raise an error if the user specified a list of experiment names # but we found several .jsons in the same directory if experiment_name and len(jsons) > 1: raise ValueError("{} seems to contain the output of multiple experiments. " "In order to use custom experiment names, you must have " "a separate directory " "for each experiment".format(full_path_experiment_dir)) # return [(json, experiment_name)] when we have experiment name or # [(json, None)] if no experiment name has been specified. # If the folder contains the output of multiple experiments, return # [(json1, None), (json2, None) .... ] return list(zip(jsons, [experiment_name] * len(jsons)))
def test_locate_files_works(self): config_dir = 'temp_output' os.makedirs(config_dir, exist_ok=True) paths = 'file1.csv' full_path = os.path.abspath(os.path.join(config_dir, paths)) open(full_path, 'a').close() result = DataReader.locate_files(paths, config_dir) rmtree(config_dir) eq_(result, full_path)
def test_locate_files_works(self): config_dir = 'temp_output' os.makedirs(config_dir, exist_ok=True) paths = 'file1.csv' full_path = os.path.abspath(os.path.join(config_dir, paths)) open(full_path, 'a').close() result = DataReader.locate_files(paths, config_dir) rmtree(config_dir) eq_(result, full_path)
def check_read_from_file(self, extension): """Test whether ``read_from_file()`` works as expected.""" name = TestDataReader.make_file_from_ext(self.df_train, extension) # now read in the file using `read_data_file()` df_read = DataReader.read_from_file(name, converters={'id': str, 'candidate': str}) # Make sure we get rid of the file at the end, # at least if we get to this point (i.e. no errors raised) self.filepaths.append(name) assert_frame_equal(self.df_train, df_read)
def check_read_from_file(self, extension): """ Test whether the ``read_from_file()`` method works as expected. """ name = TestDataReader.make_file_from_ext(self.df_train, extension) # now read in the file using `read_data_file()` df_read = DataReader.read_from_file(name, converters={'id': str, 'candidate': str}) # Make sure we get rid of the file at the end, # at least if we get to this point (i.e. no errors raised) self.filepaths.append(name) assert_frame_equal(self.df_train, df_read)
def check_experiment_dir(experiment_dir, configpath): """ Check that the supplied experiment directory exists and contains the output of the rsmtool experiment. Parameters ---------- experiment_dir : str Supplied path to the experiment_dir. configpath : str Path to the directory containing the configuration file. Returns ------- jsons : list A list paths to all configuration json files contained in the output directory Raises ------ FileNotFoundError If the directory does not exist or does not contain and output of an RSMTool experiment. """ full_path_experiment_dir = DataReader.locate_files(experiment_dir, configpath) if not full_path_experiment_dir: raise FileNotFoundError("The directory {} " "does not exist.".format(experiment_dir)) else: # check that there is an output directory csvdir = normpath(join(full_path_experiment_dir, 'output')) if not exists(csvdir): raise FileNotFoundError( "The directory {} does not contain " "the output of an rsmtool " "experiment.".format(full_path_experiment_dir)) # find the json configuration files for all experiments stored in this directory jsons = glob.glob(join(csvdir, '*.json')) if len(jsons) == 0: raise FileNotFoundError( "The directory {} does not contain " "the .json configuration files for rsmtool " "experiments.".format(full_path_experiment_dir)) return jsons
def check_experiment_dir(experiment_dir, configpath): """ Check that the supplied experiment directory exists and contains the output of the rsmtool experiment. Parameters ---------- experiment_dir : str Supplied path to the experiment_dir. configpath : str Path to the directory containing the configuration file. Returns ------- jsons : list A list paths to all configuration json files contained in the output directory Raises ------ FileNotFoundError If the directory does not exist or does not contain and output of an RSMTool experiment. """ full_path_experiment_dir = DataReader.locate_files(experiment_dir, configpath) if not full_path_experiment_dir: raise FileNotFoundError("The directory {} " "does not exist.".format(experiment_dir)) else: # check that there is an output directory csvdir = normpath(join(full_path_experiment_dir, 'output')) if not exists(csvdir): raise FileNotFoundError("The directory {} does not contain " "the output of an rsmtool " "experiment.".format(full_path_experiment_dir)) # find the json configuration files for all experiments stored in this directory jsons = glob.glob(join(csvdir, '*.json')) if len(jsons) == 0: raise FileNotFoundError("The directory {} does not contain " "the .json configuration files for rsmtool " "experiments.".format(full_path_experiment_dir)) return jsons
def check_file_output(file1, file2, file_format='csv'): """ Check if two experiment files have values that are the same to within three decimal places. Raises an AssertionError if they are not. Parameters ---------- file1 : str Path to the first file. file2 : str Path to the second files. file_format : str, optional The format of the output files. Defaults to 'csv'. """ # make sure that the main id columns are read as strings since # this may affect merging in custom notebooks string_columns = ['spkitemid', 'candidate'] converter_dict = {column: str for column in string_columns} df1 = DataReader.read_from_file(file1, converters=converter_dict) df2 = DataReader.read_from_file(file2, converters=converter_dict) # if the first column is numeric, just force the index to string; # however, if it is non-numeric, set it as the index and then # force it to string. We do this to ensure string indices are # preserved as such for df in [df1, df2]: if np.issubdtype(df[df.columns[0]].dtype, np.number): df.index = df.index.map(str) else: df.index = df[df.columns[0]] df.index = df.index.map(str) # sort all the indices alphabetically df1.sort_index(inplace=True) df2.sort_index(inplace=True) # convert any integer columns to floats in either data frame for df in [df1, df2]: for c in df.columns: if df[c].dtype == np.int64: df[c] = df[c].astype(np.float64) # do the same for indices for df in [df1, df2]: if df.index.dtype == np.int64: df.index = df.index.astype(np.float64) # for pca and factor correlations convert all values to absolutes # because the sign may not always be the same if (file1.endswith('pca.{}'.format(file_format)) or file1.endswith('factor_correlations.{}'.format(file_format))): for df in [df1, df2]: msk = df.dtypes == np.float64 df.loc[:, msk] = df.loc[:, msk].abs() try: assert_frame_equal(df1.sort_index(axis=1), df2.sort_index(axis=1), check_exact=False, check_less_precise=True) except AssertionError as e: message = e.args[0] new_message = 'File {} - {}'.format(basename(file1), message) e.args = (new_message, ) raise
def test_setup_none_in_path(self): paths = ['path1.csv', None, 'path2.csv'] framenames = ['train', 'test', 'features'] DataReader(paths, framenames)
def load_rsmtool_output(self, filedir, figdir, experiment_id, prefix, groups_eval): """ Function to load all of the outputs of an rsmtool experiment. For each type of output, we first check whether the file exists to allow comparing experiments with different sets of outputs. Parameters ---------- filedir : str Path to the directory containing output files. figdir : str Path to the directory containing output figures. experiment_id : str Original ``experiment_id`` used to generate the output files. prefix: str Must be set to ``scale`` or ``raw``. Indicates whether the score is scaled or not. groups_eval: list List of subgroup names used for subgroup evaluation. Returns ------- files : dict A dictionary with outputs converted to pandas data frames. If a particular type of output did not exist for the experiment, its value will be an empty data frame. figs: dict A dictionary with experiment figures. """ file_format = get_output_directory_extension(filedir, experiment_id) files = defaultdict(pd.DataFrame) figs = {} # feature distributions and the inter-feature correlations feature_train_file = join(filedir, '{}_train_features.{}'.format(experiment_id, file_format)) if exists(feature_train_file): files['df_train_features'] = DataReader.read_from_file(feature_train_file) feature_distplots_file = join(figdir, '{}_distrib.svg'.format(experiment_id)) if exists(feature_distplots_file): figs['feature_distplots'] = feature_distplots_file # with open(feature_distplots_file, 'rb') as f: # figs['feature_distplots'] = base64.b64encode(f.read()).decode('utf-8') feature_cors_file = join(filedir, '{}_cors_processed.{}'.format(experiment_id, file_format)) if exists(feature_cors_file): files['df_feature_cors'] = DataReader.read_from_file(feature_cors_file, index_col=0) # df_scores scores_file = join(filedir, '{}_pred_processed.{}'.format(experiment_id, file_format)) if exists(scores_file): df_scores = DataReader.read_from_file(scores_file, converters={'spkitemid': str}) files['df_scores'] = df_scores[['spkitemid', 'sc1', prefix]] # model coefficients if present betas_file = join(filedir, '{}_betas.{}'.format(experiment_id, file_format)) if exists(betas_file): files['df_coef'] = DataReader.read_from_file(betas_file, index_col=0) files['df_coef'].index.name = None # read in the model fit files if present model_fit_file = join(filedir, '{}_model_fit.{}'.format(experiment_id, file_format)) if exists(model_fit_file): files['df_model_fit'] = DataReader.read_from_file(model_fit_file) # human human agreement consistency_file = join(filedir, '{}_consistency.{}'.format(experiment_id, file_format)) # load if consistency file is present if exists(consistency_file): df_consistency = DataReader.read_from_file(consistency_file, index_col=0) files['df_consistency'] = df_consistency # degradation degradation_file = join(filedir, "{}_degradation.{}".format(experiment_id, file_format)) # load if degradation file is present if exists(degradation_file): df_degradation = DataReader.read_from_file(degradation_file, index_col=0) files['df_degradation'] = df_degradation # disattenuated correlations dis_corr_file = join(filedir, "{}_disattenuated_correlations.{}".format(experiment_id, file_format)) # load if disattenuated correlations is present if exists(dis_corr_file): df_dis_corr = DataReader.read_from_file(dis_corr_file, index_col=0) # we only use the row for raw_trim or scale_trim score files['df_disattenuated_correlations'] = df_dis_corr.loc[['{}_trim'.format(prefix)]] # read in disattenuated correlations by group for group in groups_eval: group_dis_corr_file = join(filedir, '{}_disattenuated_correlations_by_{}.{}'.format(experiment_id, group, file_format)) if exists(group_dis_corr_file): df_dis_cor_group = DataReader.read_from_file(group_dis_corr_file, index_col=0) files['df_disattenuated_correlations_by_{}'.format(group)] = df_dis_cor_group files['df_disattenuated_correlations_by_{}_overview'.format(group)] = self.make_summary_stat_df(df_dis_cor_group) # use the raw columns or the scale columns depending on the prefix existing_eval_cols = (_df_eval_columns_existing_raw if prefix == 'raw' else _df_eval_columns_existing_scale) rename_dict = raw_rename_dict if prefix == 'raw' else scale_rename_dict # read in the short version of the evaluation metrics for all data short_metrics_list = ["N", "Adj. Agmt.(br)", "Agmt.(br)", "K(br)", "Pearson(b)", "QWK(br)", "R2(b)", "RMSE(b)"] eval_file_short = join(filedir, '{}_eval_short.{}'.format(experiment_id, file_format)) if exists(eval_file_short): df_eval = DataReader.read_from_file(eval_file_short, index_col=0) df_eval = df_eval[existing_eval_cols] df_eval = df_eval.rename(columns=rename_dict) files['df_eval'] = df_eval[short_metrics_list] files['df_eval'].index.name = None eval_file = join(filedir, '{}_eval.{}'.format(experiment_id, file_format)) if exists(eval_file): files['df_eval_for_degradation'] = DataReader.read_from_file(eval_file, index_col=0) # read in the evaluation metrics by subgroup, if we are asked to for group in groups_eval: group_eval_file = join(filedir, '{}_eval_by_{}.{}'.format(experiment_id, group, file_format)) if exists(group_eval_file): df_eval = DataReader.read_from_file(group_eval_file, index_col=0) df_eval = df_eval[existing_eval_cols] df_eval = df_eval.rename(columns=rename_dict) files['df_eval_by_{}'.format(group)] = df_eval[short_metrics_list] files['df_eval_by_{}'.format(group)].index.name = None series = files['df_eval_by_{}'.format(group)] files['df_eval_by_{}_overview'.format(group)] = self.make_summary_stat_df(series) # set the ordering of mean/SD/SMD statistics files['df_eval_by_{}_m_sd'.format(group)] = df_eval[['N', 'H1 mean', 'H1 SD', 'score mean(br)', 'score SD(br)', 'score mean(b)', 'score SD(b)', 'SMD(br)', 'SMD(b)']] files['df_eval_by_{}_m_sd'.format(group)].index.name = None # read in the partial correlations vs. score for all data pcor_score_file = join(filedir, '{}_pcor_score_all_data.{}'.format(experiment_id, file_format)) if exists(pcor_score_file): files['df_pcor_sc1'] = DataReader.read_from_file(pcor_score_file, index_col=0) files['df_pcor_sc1_overview'] = self.make_summary_stat_df(files['df_pcor_sc1']) # read in the partial correlations by subgroups, if we are asked to for group in groups_eval: group_pcor_file = join(filedir, '{}_pcor_score_by_{}.{}'.format(experiment_id, group, file_format)) if exists(group_pcor_file): files['df_pcor_sc1_by_{}' ''.format(group)] = DataReader.read_from_file(group_pcor_file, index_col=0) series = files['df_pcor_sc1_by_{}'.format(group)] files['df_pcor_sc1_{}_overview'.format(group)] = self.make_summary_stat_df(series) # read in the marginal correlations vs. score for all data mcor_score_file = join(filedir, '{}_margcor_score_all_data.{}'.format(experiment_id, file_format)) if exists(mcor_score_file): files['df_mcor_sc1'] = DataReader.read_from_file(mcor_score_file, index_col=0) files['df_mcor_sc1_overview'] = self.make_summary_stat_df(files['df_mcor_sc1']) # read in the partial correlations by subgroups, if we are asked to for group in groups_eval: group_mcor_file = join(filedir, '{}_margcor_score_by_{}.{}'.format(experiment_id, group, file_format)) if exists(group_mcor_file): files['df_mcor_sc1_by_{}' ''.format(group)] = DataReader.read_from_file(group_mcor_file, index_col=0) series = files['df_mcor_sc1_by_{}'.format(group)] files['df_mcor_sc1_{}_overview'.format(group)] = self.make_summary_stat_df(series) pca_file = join(filedir, '{}_pca.{}'.format(experiment_id, file_format)) if exists(pca_file): files['df_pca'] = DataReader.read_from_file(pca_file, index_col=0) files['df_pcavar'] = DataReader.read_from_file(join(filedir, '{}_pcavar.{}'.format(experiment_id, file_format)), index_col=0) descriptives_file = join(filedir, '{}_feature_descriptives.{}'.format(experiment_id, file_format)) if exists(descriptives_file): # we read all files pertaining to the descriptive analysis together # since we merge the outputs files['df_descriptives'] = DataReader.read_from_file(descriptives_file, index_col=0) # this df contains only the number of features. this is used later # for another two tables to show the number of features df_features_n_values = files['df_descriptives'][['N', 'min', 'max']] files['df_descriptives'] = files['df_descriptives'][['N', 'mean', 'std. dev.', 'skewness', 'kurtosis']] outliers_file = join(filedir, '{}_feature_outliers.{}'.format(experiment_id, file_format)) df_outliers = DataReader.read_from_file(outliers_file, index_col=0) df_outliers = df_outliers.rename(columns={'upper': 'Upper', 'lower': 'Lower', 'both': 'Both', 'upperperc': 'Upper %', 'lowerperc': 'Lower %', 'bothperc': 'Both %'}) df_outliers_columns = df_outliers.columns.tolist() files['df_outliers'] = df_outliers # join with df_features_n_values to get the value of N files['df_outliers'] = pd.merge(files['df_outliers'], df_features_n_values, left_index=True, right_index=True)[['N'] + df_outliers_columns] # join with df_features_n_values to get the value of N percentiles_file = join(filedir, '{}_feature_descriptives' 'Extra.{}'.format(experiment_id, file_format)) files['df_percentiles'] = DataReader.read_from_file(percentiles_file, index_col=0) files['df_percentiles'] = pd.merge(files['df_percentiles'], df_features_n_values, left_index=True, right_index=True) mild_outliers = (files['df_percentiles']["Mild outliers"] / files['df_percentiles']["N"].astype(float) * 100) files['df_percentiles']["Mild outliers (%)"] = mild_outliers extreme_outliers = (files['df_percentiles']["Extreme outliers"] / files['df_percentiles']["N"].astype(float) * 100) files['df_percentiles']["Extreme outliers (%)"] = extreme_outliers files['df_percentiles'] = files['df_percentiles'][['N', 'min', 'max', '1%', '5%', '25%', '50%', '75%', '95%', '99%', 'IQR', 'Mild outliers', 'Mild outliers (%)', 'Extreme outliers', 'Extreme outliers (%)']] confmatrix_file = join(filedir, '{}_confMatrix.{}'.format(experiment_id, file_format)) if exists(confmatrix_file): conf_matrix = DataReader.read_from_file(confmatrix_file, index_col=0) files['df_confmatrix'] = self.process_confusion_matrix(conf_matrix) score_dist_file = join(filedir, '{}_score_dist.{}'.format(experiment_id, file_format)) if exists(score_dist_file): df_score_dist = DataReader.read_from_file(score_dist_file, index_col=1) df_score_dist.rename(columns={'sys_{}'.format(prefix): 'sys'}, inplace=True) files['df_score_dist'] = df_score_dist[['human', 'sys', 'difference']] # read in the feature boxplots by subgroup, if we were asked to for group in groups_eval: feature_boxplot_prefix = join(figdir, '{}_feature_boxplot_by_{}'.format(experiment_id, group)) svg_file = join(feature_boxplot_prefix + '.svg') png_file = join(feature_boxplot_prefix + '.png') if exists(svg_file): figs['feature_boxplots_by_{}_svg'.format(group)] = svg_file elif exists(png_file): figs['feature_boxplots_by_{}_png'.format(group)] = png_file # read in the betas image if exists betas_svg = join(figdir, '{}_betas.svg'.format(experiment_id)) if exists(betas_svg): figs['betas'] = betas_svg # read in the evaluation barplots by subgroup, if we were asked to for group in groups_eval: eval_barplot_svg_file = join(figdir, '{}_eval_by_{}.svg'.format(experiment_id, group)) if exists(eval_barplot_svg_file): figs['eval_barplot_by_{}'.format(group)] = eval_barplot_svg_file pca_svg_file = join(figdir, '{}_pca.svg'.format(experiment_id)) if exists(pca_svg_file): figs['pca_scree_plot'] = pca_svg_file return (files, figs, file_format)
def run_experiment(config_file_or_obj, output_dir): """ Run RSMTool experiment using the given configuration file and generate all outputs in the given directory. Parameters ---------- config_file_or_obj : str or Configuration Path to the experiment configuration file. Users can also pass a `Configuration` object that is in memory. output_dir : str Path to the experiment output directory. Raises ------ ValueError If any of the required fields are missing or ill-specified. """ logger = logging.getLogger(__name__) # create the 'output' and the 'figure' sub-directories # where all the experiment output such as the CSV files # and the box plots will be saved # Get absolute paths to output directories csvdir = abspath(join(output_dir, 'output')) figdir = abspath(join(output_dir, 'figure')) reportdir = abspath(join(output_dir, 'report')) featuredir = abspath(join(output_dir, 'feature')) # Make directories, if necessary makedirs(csvdir, exist_ok=True) makedirs(figdir, exist_ok=True) makedirs(reportdir, exist_ok=True) # Allow users to pass Configuration object to the # `config_file_or_obj` argument, rather than read from file if not isinstance(config_file_or_obj, Configuration): # Instantiate configuration parser object parser = ConfigurationParser.get_configparser(config_file_or_obj) configuration = parser.read_normalize_validate_and_process_config(config_file_or_obj) # get the directory where the configuration file lives configpath = dirname(config_file_or_obj) else: configuration = config_file_or_obj if configuration.filepath is not None: configpath = dirname(configuration.filepath) else: configpath = getcwd() logger.info('Saving configuration file.') configuration.save(output_dir) # Get output format file_format = configuration.get('file_format', 'csv') # Get DataWriter object writer = DataWriter(configuration['experiment_id']) # Get the paths and names for the DataReader (file_names, file_paths_org) = configuration.get_names_and_paths(['train_file', 'test_file', 'features', 'feature_subset_file'], ['train', 'test', 'feature_specs', 'feature_subset_specs']) file_paths = DataReader.locate_files(file_paths_org, configpath) # if there are any missing files after trying to locate # all expected files, raise an error if None in file_paths: missing_file_paths = [file_paths_org[idx] for idx, path in enumerate(file_paths) if path is None] raise FileNotFoundError('The following files were not found: ' '{}'.format(repr(missing_file_paths))) # Use the default converter for both train and test converters = {'train': configuration.get_default_converter(), 'test': configuration.get_default_converter()} logger.info('Reading in all data from files.') # Initialize the reader reader = DataReader(file_paths, file_names, converters) data_container = reader.read() logger.info('Preprocessing all features.') # Initialize the processor processor = FeaturePreprocessor() (processed_config, processed_container) = processor.process_data(configuration, data_container) # Rename certain frames with more descriptive names # for writing out experiment files rename_dict = {'train_excluded': 'train_excluded_responses', 'test_excluded': 'test_excluded_responses', 'train_length': 'train_response_lengths', 'train_flagged': 'train_responses_with_excluded_flags', 'test_flagged': 'test_responses_with_excluded_flags'} logger.info('Saving training and test set data to disk.') # Write out files writer.write_experiment_output(csvdir, processed_container, ['train_features', 'test_features', 'train_metadata', 'test_metadata', 'train_other_columns', 'test_other_columns', 'train_preprocessed_features', 'test_preprocessed_features', 'train_excluded', 'test_excluded', 'train_length', 'test_human_scores', 'train_flagged', 'test_flagged'], rename_dict, file_format=file_format) # Initialize the analyzer analyzer = Analyzer() (analyzed_config, analyzed_container) = analyzer.run_data_composition_analyses_for_rsmtool(processed_container, processed_config) # Write out files writer.write_experiment_output(csvdir, analyzed_container, file_format=file_format) logger.info('Training {} model.'.format(processed_config['model_name'])) # Initialize modeler modeler = Modeler() modeler.train(processed_config, processed_container, csvdir, figdir, file_format) # Identify the features used by the model selected_features = modeler.get_feature_names() # Add selected features to processed configuration processed_config['selected_features'] = selected_features # Write out files writer.write_feature_csv(featuredir, processed_container, selected_features, file_format=file_format) features_data_container = processed_container.copy() # Get selected feature info, and write out to file df_feature_info = features_data_container.feature_info.copy() df_selected_feature_info = df_feature_info[df_feature_info['feature'].isin(selected_features)] selected_feature_dataset_dict = {'name': 'selected_feature_info', 'frame': df_selected_feature_info} features_data_container.add_dataset(selected_feature_dataset_dict, update=True) writer.write_experiment_output(csvdir, features_data_container, dataframe_names=['selected_feature_info'], new_names_dict={'selected_feature_info': 'feature'}, file_format=file_format) logger.info('Running analyses on training set.') (train_analyzed_config, train_analyzed_container) = analyzer.run_training_analyses(processed_container, processed_config) # Write out files writer.write_experiment_output(csvdir, train_analyzed_container, reset_index=True, file_format=file_format) # Use only selected features for predictions columns_for_prediction = ['spkitemid', 'sc1'] + selected_features train_for_prediction = processed_container.train_preprocessed_features[columns_for_prediction] test_for_prediction = processed_container.test_preprocessed_features[columns_for_prediction] logged_str = 'Generating training and test set predictions' logged_str += ' (expected scores).' if configuration['predict_expected_scores'] else '.' logger.info(logged_str) (pred_config, pred_data_container) = modeler.predict_train_and_test(train_for_prediction, test_for_prediction, processed_config) # Write out files writer.write_experiment_output(csvdir, pred_data_container, new_names_dict={'pred_test': 'pred_processed'}, file_format=file_format) original_coef_file = join(csvdir, '{}_coefficients.{}'.format(pred_config['experiment_id'], file_format)) # If coefficients file exists, then generate # scaled coefficients and save to file if exists(original_coef_file): logger.info('Scaling the coefficients and saving them to disk') try: # Scale coefficients, and return DataContainer w/ scaled coefficients scaled_data_container = modeler.scale_coefficients(pred_config) # Write out files to disk writer.write_experiment_output(csvdir, scaled_data_container, file_format=file_format) except AttributeError: raise ValueError("It appears you are trying to save two different " "experiments to the same directory using the same " "ID. Please clear the content of the directory and " "rerun both experiments using different " "experiment IDs.") # Add processed data_container frames to pred_data_container new_pred_data_container = pred_data_container + processed_container logger.info('Running prediction analyses.') (pred_analysis_config, pred_analysis_data_container) = analyzer.run_prediction_analyses(new_pred_data_container, pred_config) # Write out files writer.write_experiment_output(csvdir, pred_analysis_data_container, reset_index=True, file_format=file_format) # Initialize reporter reporter = Reporter() # generate the report logger.info('Starting report generation.') reporter.create_report(processed_config, csvdir, figdir)
def test_locate_files_wrong_type(self): paths = {'file1.csv', 'file2.xlsx'} config_dir = 'output' DataReader.locate_files(paths, config_dir)
def check_file_output(file1, file2, file_format='csv'): """ Check if two experiment files have values that are the same to within three decimal places. Raises an AssertionError if they are not. Parameters ---------- file1 : str Path to the first file. file2 : str Path to the second files. file_format : str, optional The format of the output files. Defaults to 'csv'. """ # make sure that the main id columns are read as strings since # this may affect merging in custom notebooks string_columns = ['spkitemid', 'candidate'] converter_dict = {column: str for column in string_columns} df1 = DataReader.read_from_file(file1, converters=converter_dict) df2 = DataReader.read_from_file(file2, converters=converter_dict) # if the first column is numeric, just force the index to string; # however, if it is non-numeric, set it as the index and then # force it to string. We do this to ensure string indices are # preserved as such for df in [df1, df2]: if np.issubdtype(df[df.columns[0]].dtype, np.number): df.index = df.index.map(str) else: df.index = df[df.columns[0]] df.index = df.index.map(str) # sort all the indices alphabetically df1.sort_index(inplace=True) df2.sort_index(inplace=True) # convert any integer columns to floats in either data frame for df in [df1, df2]: for c in df.columns: if df[c].dtype == np.int64: df[c] = df[c].astype(np.float64) # do the same for indices for df in [df1, df2]: if df.index.dtype == np.int64: df.index = df.index.astype(np.float64) # for pca and factor correlations convert all values to absolutes # because the sign may not always be the same if (file1.endswith('pca.{}'.format(file_format)) or file1.endswith('factor_correlations.{}'.format(file_format))): for df in [df1, df2]: msk = df.dtypes == np.float64 df.loc[:, msk] = df.loc[:, msk].abs() try: assert_frame_equal(df1.sort_index(axis=1), df2.sort_index(axis=1), check_exact=False, check_less_precise=True) except AssertionError as e: message = e.args[0] new_message = 'File {} - {}'.format(basename(file1), message) e.args = (new_message, ) raise
def test_locate_files_str(self): paths = 'file1.csv' config_dir = 'output' result = DataReader.locate_files(paths, config_dir) eq_(result, None)
def run_experiment(config_file_or_obj, output_dir): """ Run RSMTool experiment using the given configuration file and generate all outputs in the given directory. Parameters ---------- config_file_or_obj : str or Configuration Path to the experiment configuration file. Users can also pass a `Configuration` object that is in memory. output_dir : str Path to the experiment output directory. Raises ------ ValueError If any of the required fields are missing or ill-specified. """ logger = logging.getLogger(__name__) # create the 'output' and the 'figure' sub-directories # where all the experiment output such as the CSV files # and the box plots will be saved # Get absolute paths to output directories csvdir = abspath(join(output_dir, 'output')) figdir = abspath(join(output_dir, 'figure')) reportdir = abspath(join(output_dir, 'report')) featuredir = abspath(join(output_dir, 'feature')) # Make directories, if necessary makedirs(csvdir, exist_ok=True) makedirs(figdir, exist_ok=True) makedirs(reportdir, exist_ok=True) # Allow users to pass Configuration object to the # `config_file_or_obj` argument, rather than read from file if not isinstance(config_file_or_obj, Configuration): # Instantiate configuration parser object parser = ConfigurationParser.get_configparser(config_file_or_obj) configuration = parser.read_normalize_validate_and_process_config( config_file_or_obj) # get the directory where the configuration file lives configpath = dirname(config_file_or_obj) else: configuration = config_file_or_obj if configuration.filepath is not None: configpath = dirname(configuration.filepath) else: configpath = getcwd() logger.info('Saving configuration file.') configuration.save(output_dir) # Get output format file_format = configuration.get('file_format', 'csv') # Get DataWriter object writer = DataWriter(configuration['experiment_id']) # Get the paths and names for the DataReader (file_names, file_paths_org) = configuration.get_names_and_paths( ['train_file', 'test_file', 'features', 'feature_subset_file'], ['train', 'test', 'feature_specs', 'feature_subset_specs']) file_paths = DataReader.locate_files(file_paths_org, configpath) # if there are any missing files after trying to locate # all expected files, raise an error if None in file_paths: missing_file_paths = [ file_paths_org[idx] for idx, path in enumerate(file_paths) if path is None ] raise FileNotFoundError('The following files were not found: ' '{}'.format(repr(missing_file_paths))) # Use the default converter for both train and test converters = { 'train': configuration.get_default_converter(), 'test': configuration.get_default_converter() } logger.info('Reading in all data from files.') # Initialize the reader reader = DataReader(file_paths, file_names, converters) data_container = reader.read() logger.info('Preprocessing all features.') # Initialize the processor processor = FeaturePreprocessor() (processed_config, processed_container) = processor.process_data(configuration, data_container) # Rename certain frames with more descriptive names # for writing out experiment files rename_dict = { 'train_excluded': 'train_excluded_responses', 'test_excluded': 'test_excluded_responses', 'train_length': 'train_response_lengths', 'train_flagged': 'train_responses_with_excluded_flags', 'test_flagged': 'test_responses_with_excluded_flags' } logger.info('Saving training and test set data to disk.') # Write out files writer.write_experiment_output( csvdir, processed_container, [ 'train_features', 'test_features', 'train_metadata', 'test_metadata', 'train_other_columns', 'test_other_columns', 'train_preprocessed_features', 'test_preprocessed_features', 'train_excluded', 'test_excluded', 'train_length', 'test_human_scores', 'train_flagged', 'test_flagged' ], rename_dict, file_format=file_format) # Initialize the analyzer analyzer = Analyzer() (analyzed_config, analyzed_container) = analyzer.run_data_composition_analyses_for_rsmtool( processed_container, processed_config) # Write out files writer.write_experiment_output(csvdir, analyzed_container, file_format=file_format) logger.info('Training {} model.'.format(processed_config['model_name'])) # Initialize modeler modeler = Modeler() modeler.train(processed_config, processed_container, csvdir, figdir, file_format) # Identify the features used by the model selected_features = modeler.get_feature_names() # Add selected features to processed configuration processed_config['selected_features'] = selected_features # Write out files writer.write_feature_csv(featuredir, processed_container, selected_features, file_format=file_format) features_data_container = processed_container.copy() # Get selected feature info, and write out to file df_feature_info = features_data_container.feature_info.copy() df_selected_feature_info = df_feature_info[df_feature_info['feature'].isin( selected_features)] selected_feature_dataset_dict = { 'name': 'selected_feature_info', 'frame': df_selected_feature_info } features_data_container.add_dataset(selected_feature_dataset_dict, update=True) writer.write_experiment_output( csvdir, features_data_container, dataframe_names=['selected_feature_info'], new_names_dict={'selected_feature_info': 'feature'}, file_format=file_format) logger.info('Running analyses on training set.') (train_analyzed_config, train_analyzed_container) = analyzer.run_training_analyses( processed_container, processed_config) # Write out files writer.write_experiment_output(csvdir, train_analyzed_container, reset_index=True, file_format=file_format) # Use only selected features for predictions columns_for_prediction = ['spkitemid', 'sc1'] + selected_features train_for_prediction = processed_container.train_preprocessed_features[ columns_for_prediction] test_for_prediction = processed_container.test_preprocessed_features[ columns_for_prediction] logged_str = 'Generating training and test set predictions' logged_str += ' (expected scores).' if configuration[ 'predict_expected_scores'] else '.' logger.info(logged_str) (pred_config, pred_data_container) = modeler.predict_train_and_test( train_for_prediction, test_for_prediction, processed_config) # Write out files writer.write_experiment_output( csvdir, pred_data_container, new_names_dict={'pred_test': 'pred_processed'}, file_format=file_format) original_coef_file = join( csvdir, '{}_coefficients.{}'.format(pred_config['experiment_id'], file_format)) # If coefficients file exists, then generate # scaled coefficients and save to file if exists(original_coef_file): logger.info('Scaling the coefficients and saving them to disk') try: # Scale coefficients, and return DataContainer w/ scaled coefficients scaled_data_container = modeler.scale_coefficients(pred_config) # Write out files to disk writer.write_experiment_output(csvdir, scaled_data_container, file_format=file_format) except AttributeError: raise ValueError( "It appears you are trying to save two different " "experiments to the same directory using the same " "ID. Please clear the content of the directory and " "rerun both experiments using different " "experiment IDs.") # Add processed data_container frames to pred_data_container new_pred_data_container = pred_data_container + processed_container logger.info('Running prediction analyses.') (pred_analysis_config, pred_analysis_data_container) = analyzer.run_prediction_analyses( new_pred_data_container, pred_config) # Write out files writer.write_experiment_output(csvdir, pred_analysis_data_container, reset_index=True, file_format=file_format) # Initialize reporter reporter = Reporter() # generate the report logger.info('Starting report generation.') reporter.create_report(processed_config, csvdir, figdir)
def run_comparison(config_file_or_obj, output_dir): """ Run an ``rsmcompare`` experiment using the given configuration file and generate the report in the given directory. Parameters ---------- config_file_or_obj : str or Configuration Path to the experiment configuration file. Users can also pass a `Configuration` object that is in memory. output_dir : str Path to the experiment output directory. Raises ------ ValueError If any of the required fields are missing or ill-specified. """ logger = logging.getLogger(__name__) # Allow users to pass Configuration object to the # `config_file_or_obj` argument, rather than read file if not isinstance(config_file_or_obj, Configuration): # Instantiate configuration parser object parser = ConfigurationParser.get_configparser(config_file_or_obj) configuration = parser.read_normalize_validate_and_process_config( config_file_or_obj, context='rsmcompare') # get the directory where the configuration file lives configpath = dirname(config_file_or_obj) else: configuration = config_file_or_obj if configuration.filepath is not None: configpath = dirname(configuration.filepath) else: configpath = os.getcwd() logger.info('Saving configuration file.') configuration.save(output_dir) # get the information about the "old" experiment experiment_id_old = configuration['experiment_id_old'] experiment_dir_old = DataReader.locate_files( configuration['experiment_dir_old'], configpath) if not experiment_dir_old: raise FileNotFoundError("The directory {} " "does not exist.".format( configuration['experiment_dir_old'])) else: csvdir_old = normpath(join(experiment_dir_old, 'output')) figdir_old = normpath(join(experiment_dir_old, 'figure')) if not exists(csvdir_old) or not exists(figdir_old): raise FileNotFoundError("The directory {} does not contain " "the output of an rsmtool " "experiment.".format(experiment_dir_old)) check_experiment_id(experiment_dir_old, experiment_id_old) # get the information about the "new" experiment experiment_id_new = configuration['experiment_id_new'] experiment_dir_new = DataReader.locate_files( configuration['experiment_dir_new'], configpath) if not experiment_dir_new: raise FileNotFoundError("The directory {} " "does not exist.".format( configuration['experiment_dir_new'])) else: csvdir_new = normpath(join(experiment_dir_new, 'output')) figdir_new = normpath(join(experiment_dir_new, 'figure')) if not exists(csvdir_new) or not exists(figdir_new): raise FileNotFoundError("The directory {} does not contain " "the output of an rsmtool " "experiment.".format(experiment_dir_new)) check_experiment_id(experiment_dir_new, experiment_id_new) # are there specific general report sections we want to include? general_report_sections = configuration['general_sections'] # what about the special or custom sections? special_report_sections = configuration['special_sections'] custom_report_section_paths = configuration['custom_sections'] # if custom report sections exist, locate sections; otherwise, create empty list if custom_report_section_paths: logger.info('Locating custom report sections') custom_report_sections = Reporter.locate_custom_sections( custom_report_section_paths, configpath) else: custom_report_sections = [] # get the section order section_order = configuration['section_order'] # get the subgroups if any subgroups = configuration.get('subgroups') # Initialize reporter reporter = Reporter() chosen_notebook_files = reporter.get_ordered_notebook_files( general_report_sections, special_report_sections, custom_report_sections, section_order, subgroups, model_type=None, context='rsmcompare') # add chosen notebook files to configuration configuration['chosen_notebook_files'] = chosen_notebook_files # now generate the comparison report logger.info('Starting report generation.') reporter.create_comparison_report(configuration, csvdir_old, figdir_old, csvdir_new, figdir_new, output_dir)
def run_evaluation(config_file_or_obj, output_dir): """ Run an `rsmeval` experiment using the given configuration file and generate all outputs in the given directory. Parameters ---------- config_file_or_obj : str or configuration_parser.Configuration Path to the experiment configuration file. Users can also pass a `Configuration` object that is in memory. output_dir : str Path to the experiment output directory. Raises ------ ValueError If any of the required fields are missing or ill-specified. """ logger = logging.getLogger(__name__) # create the 'output' and the 'figure' sub-directories # where all the experiment output such as the CSV files # and the box plots will be saved csvdir = abspath(join(output_dir, 'output')) figdir = abspath(join(output_dir, 'figure')) reportdir = abspath(join(output_dir, 'report')) os.makedirs(csvdir, exist_ok=True) os.makedirs(figdir, exist_ok=True) os.makedirs(reportdir, exist_ok=True) # Allow users to pass Configuration object to the # `config_file_or_obj` argument, rather than read file if not isinstance(config_file_or_obj, Configuration): # Instantiate configuration parser object parser = ConfigurationParser.get_configparser(config_file_or_obj) configuration = parser.read_normalize_validate_and_process_config( config_file_or_obj, context='rsmeval') # get the directory where the configuration file lives configpath = dirname(config_file_or_obj) else: configuration = config_file_or_obj if configuration.filepath is not None: configpath = dirname(configuration.filepath) else: configpath = os.getcwd() logger.info('Saving configuration file.') configuration.save(output_dir) # Get output format file_format = configuration.get('file_format', 'csv') # Get DataWriter object writer = DataWriter(configuration['experiment_id']) # Make sure prediction file can be located if not DataReader.locate_files(configuration['predictions_file'], configpath): raise FileNotFoundError('Error: Predictions file {} ' 'not found.\n'.format( configuration['predictions_file'])) scale_with = configuration.get('scale_with') # scale_with can be one of the following: # (a) None : the predictions are assumed to be 'raw' and should be used as is # when computing the metrics; the names for the final columns are # 'raw', 'raw_trim' and 'raw_trim_round'. # (b) 'asis' : the predictions are assumed to be pre-scaled and should be used as is # when computing the metrics; the names for the final columns are # 'scale', 'scale_trim' and 'scale_trim_round'. # (c) a CSV file : the predictions are assumed to be 'raw' and should be scaled # before computing the metrics; the names for the final columns are # 'scale', 'scale_trim' and 'scale_trim_round'. # Check whether we want to do scaling do_scaling = (scale_with is not None and scale_with != 'asis') # The paths to files and names for data container properties paths = ['predictions_file'] names = ['predictions'] # If we want to do scaling, get the scale file if do_scaling: # Make sure scale file can be located scale_file_location = DataReader.locate_files(scale_with, configpath) if not scale_file_location: raise FileNotFoundError('Could not find scaling file {}.' ''.format(scale_file_location)) paths.append('scale_with') names.append('scale') # Get the paths, names, and converters for the DataReader (file_names, file_paths) = configuration.get_names_and_paths(paths, names) file_paths = DataReader.locate_files(file_paths, configpath) converters = {'predictions': configuration.get_default_converter()} logger.info('Reading predictions: {}.'.format( configuration['predictions_file'])) # Initialize the reader reader = DataReader(file_paths, file_names, converters) data_container = reader.read() logger.info('Preprocessing predictions.') # Initialize the processor processor = FeaturePreprocessor() (processed_config, processed_container) = processor.process_data(configuration, data_container, context='rsmeval') logger.info('Saving pre-processed predictions and metadata to disk.') writer.write_experiment_output(csvdir, processed_container, new_names_dict={ 'pred_test': 'pred_processed', 'test_excluded': 'test_excluded_responses' }, file_format=file_format) # Initialize the analyzer analyzer = Analyzer() # do the data composition stats (analyzed_config, analyzed_container) = analyzer.run_data_composition_analyses_for_rsmeval( processed_container, processed_config) # Write out files writer.write_experiment_output(csvdir, analyzed_container, file_format=file_format) for_pred_data_container = analyzed_container + processed_container # run the analyses on the predictions of the model` logger.info('Running analyses on predictions.') (pred_analysis_config, pred_analysis_data_container) = analyzer.run_prediction_analyses( for_pred_data_container, analyzed_config) writer.write_experiment_output(csvdir, pred_analysis_data_container, reset_index=True, file_format=file_format) # Initialize reporter reporter = Reporter() # generate the report logger.info('Starting report generation.') reporter.create_report(processed_config, csvdir, figdir, context='rsmeval')
def compute_and_save_predictions(config_file_or_obj, output_file, feats_file=None): """ Run ``rsmpredict`` with given configuration file and generate predictions (and, optionally, pre-processed feature values). Parameters ---------- config_file_or_obj : str or configuration_parser.Configuration Path to the experiment configuration file. Users can also pass a `Configuration` object that is in memory. output_dir : str Path to the output directory for saving files. feats_file (optional): str Path to the output file for saving preprocessed feature values. Raises ------ ValueError If any of the required fields are missing or ill-specified. """ logger = logging.getLogger(__name__) # Allow users to pass Configuration object to the # `config_file_or_obj` argument, rather than read file if not isinstance(config_file_or_obj, Configuration): # Instantiate configuration parser object parser = ConfigurationParser.get_configparser(config_file_or_obj) config = parser.read_normalize_validate_and_process_config(config_file_or_obj, context='rsmpredict') # get the directory where the config file lives configpath = dirname(config_file_or_obj) else: config = config_file_or_obj if config.filepath is not None: configpath = dirname(config.filepath) else: configpath = os.getcwd() # get the experiment ID experiment_id = config['experiment_id'] # Get output format file_format = config.get('file_format', 'csv') # Get DataWriter object writer = DataWriter(experiment_id) # get the input file containing the feature values # for which we want to generate the predictions input_features_file = DataReader.locate_files(config['input_features_file'], configpath) if not input_features_file: raise FileNotFoundError('Input file {} does not exist' ''.format(config['input_features_file'])) experiment_dir = DataReader.locate_files(config['experiment_dir'], configpath) if not experiment_dir: raise FileNotFoundError('The directory {} does not exist.' ''.format(config['experiment_dir'])) else: experiment_output_dir = normpath(join(experiment_dir, 'output')) if not exists(experiment_output_dir): raise FileNotFoundError('The directory {} does not contain ' 'the output of an rsmtool experiment.'.format(experiment_dir)) # find all the .model files in the experiment output directory model_files = glob.glob(join(experiment_output_dir, '*.model')) if not model_files: raise FileNotFoundError('The directory {} does not contain any rsmtool models.' ''.format(experiment_output_dir)) experiment_ids = [splitext(basename(mf))[0] for mf in model_files] if experiment_id not in experiment_ids: raise FileNotFoundError('{} does not contain a model for the experiment "{}". ' 'The following experiments are contained in this ' 'directory: {}'.format(experiment_output_dir, experiment_id, experiment_ids)) # check that the directory contains outher required files required_file_types = ['feature', 'postprocessing_params'] for file_type in required_file_types: expected_file_name = "{}_{}.csv".format(experiment_id, file_type) if not exists(join(experiment_output_dir, expected_file_name)): raise FileNotFoundError('{} does not contain the required file ' '{} that was generated during the ' 'original model training'.format(experiment_output_dir, expected_file_name)) # model_files = glob.glob(join(experiment_output_dir, '*.model')) # if not model_files: # raise FileNotFoundError('The directory {} does not contain any rsmtool models. ' # ''.format(experiment_output_dir)) logger.info('Reading input files.') feature_info = join(experiment_output_dir, '{}_feature.csv'.format(experiment_id)) post_processing = join(experiment_output_dir, '{}_postprocessing_params.csv'.format(experiment_id)) file_paths = [input_features_file, feature_info, post_processing] file_names = ['input_features', 'feature_info', 'postprocessing_params'] converters = {'input_features': config.get_default_converter()} # Initialize the reader reader = DataReader(file_paths, file_names, converters) data_container = reader.read(kwargs_dict={'feature_info': {'index_col': 0}}) # load the Modeler to generate the predictions model = Modeler.load_from_file(join(experiment_output_dir, '{}.model'.format(experiment_id))) # Add the model to the configuration object config['model'] = model # Initialize the processor processor = FeaturePreprocessor() (processed_config, processed_container) = processor.process_data(config, data_container, context='rsmpredict') # save the pre-processed features to disk if we were asked to if feats_file is not None: logger.info('Saving pre-processed feature values to {}'.format(feats_file)) feats_dir = dirname(feats_file) # create any directories needed for the output file os.makedirs(feats_dir, exist_ok=True) _, feats_filename = split(feats_file) feats_filename, _ = splitext(feats_filename) # Write out files writer.write_experiment_output(feats_dir, processed_container, include_experiment_id=False, dataframe_names=['features_processed'], new_names_dict={'features_processed': feats_filename}, file_format=file_format) if (output_file.lower().endswith('.csv') or output_file.lower().endswith('.xlsx')): output_dir = dirname(output_file) _, filename = split(output_file) filename, _ = splitext(filename) else: output_dir = output_file filename = 'predictions_with_metadata' # create any directories needed for the output file os.makedirs(output_dir, exist_ok=True) # save the predictions to disk logger.info('Saving predictions.') # Write out files writer.write_experiment_output(output_dir, processed_container, include_experiment_id=False, dataframe_names=['predictions_with_metadata'], new_names_dict={'predictions_with_metadata': filename}, file_format=file_format) # save excluded responses to disk if not processed_container.excluded.empty: # save the predictions to disk logger.info('Saving excluded responses to {}'.format(join(output_dir, '{}_excluded_responses.csv' ''.format(filename)))) # Write out files writer.write_experiment_output(output_dir, processed_container, include_experiment_id=False, dataframe_names=['excluded'], new_names_dict={'excluded': '{}_excluded_responses' ''.format(filename)}, file_format=file_format)
def load_rsmtool_output(self, filedir, figdir, experiment_id, prefix, groups_eval): """ Function to load all of the outputs of an rsmtool experiment. For each type of output, we first check whether the file exists to allow comparing experiments with different sets of outputs. Parameters ---------- filedir : str Path to the directory containing output files. figdir : str Path to the directory containing output figures. experiment_id : str Original ``experiment_id`` used to generate the output files. prefix: str Must be set to ``scale`` or ``raw``. Indicates whether the score is scaled or not. groups_eval: list List of subgroup names used for subgroup evaluation. Returns ------- files : dict A dictionary with outputs converted to pandas data frames. If a particular type of output did not exist for the experiment, its value will be an empty data frame. figs: dict A dictionary with experiment figures. """ file_format = get_output_directory_extension(filedir, experiment_id) files = defaultdict(pd.DataFrame) figs = {} # feature distributions and the inter-feature correlations feature_train_file = join(filedir, '{}_train_features.{}'.format(experiment_id, file_format)) if exists(feature_train_file): files['df_train_features'] = DataReader.read_from_file(feature_train_file) feature_distplots_file = join(figdir, '{}_distrib.svg'.format(experiment_id)) if exists(feature_distplots_file): figs['feature_distplots'] = feature_distplots_file feature_cors_file = join(filedir, '{}_cors_processed.{}'.format(experiment_id, file_format)) if exists(feature_cors_file): files['df_feature_cors'] = DataReader.read_from_file(feature_cors_file, index_col=0) # df_scores scores_file = join(filedir, '{}_pred_processed.{}'.format(experiment_id, file_format)) if exists(scores_file): df_scores = DataReader.read_from_file(scores_file, converters={'spkitemid': str}) files['df_scores'] = df_scores[['spkitemid', 'sc1', prefix]] # model coefficients if present betas_file = join(filedir, '{}_betas.{}'.format(experiment_id, file_format)) if exists(betas_file): files['df_coef'] = DataReader.read_from_file(betas_file, index_col=0) files['df_coef'].index.name = None # read in the model fit files if present model_fit_file = join(filedir, '{}_model_fit.{}'.format(experiment_id, file_format)) if exists(model_fit_file): files['df_model_fit'] = DataReader.read_from_file(model_fit_file) # human human agreement consistency_file = join(filedir, '{}_consistency.{}'.format(experiment_id, file_format)) # load if consistency file is present if exists(consistency_file): df_consistency = DataReader.read_from_file(consistency_file, index_col=0) files['df_consistency'] = df_consistency # degradation degradation_file = join(filedir, "{}_degradation.{}".format(experiment_id, file_format)) # load if degradation file is present if exists(degradation_file): df_degradation = DataReader.read_from_file(degradation_file, index_col=0) files['df_degradation'] = df_degradation # disattenuated correlations dis_corr_file = join(filedir, "{}_disattenuated_correlations.{}".format(experiment_id, file_format)) # load if disattenuated correlations is present if exists(dis_corr_file): df_dis_corr = DataReader.read_from_file(dis_corr_file, index_col=0) # we only use the row for raw_trim or scale_trim score files['df_disattenuated_correlations'] = df_dis_corr.loc[['{}_trim'.format(prefix)]] # read in disattenuated correlations by group for group in groups_eval: group_dis_corr_file = join(filedir, '{}_disattenuated_correlations_by_{}.{}'.format(experiment_id, group, file_format)) if exists(group_dis_corr_file): df_dis_cor_group = DataReader.read_from_file(group_dis_corr_file, index_col=0) files['df_disattenuated_correlations_by_{}'.format(group)] = df_dis_cor_group files['df_disattenuated_correlations_by_{}_overview'.format(group)] = self.make_summary_stat_df(df_dis_cor_group) # true score evaluations true_score_eval_file = join(filedir, "{}_true_score_eval.{}".format(experiment_id, file_format)) # load true score evaluations if present if exists(true_score_eval_file): df_true_score_eval = DataReader.read_from_file(true_score_eval_file, index_col=0) # we only use the row for raw_trim or scale_trim score files['df_true_score_eval'] = df_true_score_eval.loc[['{}_trim'.format(prefix)]] # use the raw columns or the scale columns depending on the prefix existing_eval_cols = (_df_eval_columns_existing_raw if prefix == 'raw' else _df_eval_columns_existing_scale) rename_dict = raw_rename_dict if prefix == 'raw' else scale_rename_dict # read in the short version of the evaluation metrics for all data short_metrics_list = ["N", "Adj. Agmt.(br)", "Agmt.(br)", "K(br)", "Pearson(b)", "QWK(b)", "R2(b)", "RMSE(b)"] eval_file_short = join(filedir, '{}_eval_short.{}'.format(experiment_id, file_format)) if exists(eval_file_short): df_eval = DataReader.read_from_file(eval_file_short, index_col=0) (rename_dict_new, existing_eval_cols_new, short_metrics_list_new, _) = self._modify_eval_columns_to_ensure_version_compatibilty(df_eval, rename_dict, existing_eval_cols, short_metrics_list) df_eval = df_eval[existing_eval_cols_new] df_eval = df_eval.rename(columns=rename_dict_new) files['df_eval'] = df_eval[short_metrics_list_new] files['df_eval'].index.name = None eval_file = join(filedir, '{}_eval.{}'.format(experiment_id, file_format)) if exists(eval_file): files['df_eval_for_degradation'] = DataReader.read_from_file(eval_file, index_col=0) # read in the evaluation metrics by subgroup, if we are asked to for group in groups_eval: group_eval_file = join(filedir, '{}_eval_by_{}.{}'.format(experiment_id, group, file_format)) if exists(group_eval_file): df_eval = DataReader.read_from_file(group_eval_file, index_col=0) (rename_dict_new, existing_eval_cols_new, short_metrics_list_new, smd_name ) = self._modify_eval_columns_to_ensure_version_compatibilty(df_eval, rename_dict, existing_eval_cols, short_metrics_list, raise_warnings=False) # if `SMD` is being used, rather than `DSM`, we print a note for the user; we don't # want to go so far as to raise a warning, but we do want to give the user some info if smd_name == 'SMD': warnings.warn("The subgroup evaluations in `{}` use 'SMD'. Please note " "that newer versions of RSMTool (7.0 or greater) use 'DSM' with subgroup " "evaluations. For additional details on how these metrics " "differ, see the RSMTool documentation. Comparisons with experiments " "using SMD for subgroup calculations will be deprecated in the next major " "release.".format(group_eval_file), category=DeprecationWarning) df_eval = df_eval[existing_eval_cols_new] df_eval = df_eval.rename(columns=rename_dict_new) files['df_eval_by_{}'.format(group)] = df_eval[short_metrics_list_new] files['df_eval_by_{}'.format(group)].index.name = None series = files['df_eval_by_{}'.format(group)] files['df_eval_by_{}_overview'.format(group)] = self.make_summary_stat_df(series) # set the ordering of mean/SD/SMD statistics files['df_eval_by_{}_m_sd'.format(group)] = df_eval[['N', 'H1 mean', 'H1 SD', 'score mean(br)', 'score SD(br)', 'score mean(b)', 'score SD(b)', '{}(br)'.format(smd_name), '{}(b)'.format(smd_name)]] files['df_eval_by_{}_m_sd'.format(group)].index.name = None # read in the partial correlations vs. score for all data pcor_score_file = join(filedir, '{}_pcor_score_all_data.{}'.format(experiment_id, file_format)) if exists(pcor_score_file): files['df_pcor_sc1'] = DataReader.read_from_file(pcor_score_file, index_col=0) files['df_pcor_sc1_overview'] = self.make_summary_stat_df(files['df_pcor_sc1']) # read in the partial correlations by subgroups, if we are asked to for group in groups_eval: group_pcor_file = join(filedir, '{}_pcor_score_by_{}.{}'.format(experiment_id, group, file_format)) if exists(group_pcor_file): files['df_pcor_sc1_by_{}' ''.format(group)] = DataReader.read_from_file(group_pcor_file, index_col=0) series = files['df_pcor_sc1_by_{}'.format(group)] files['df_pcor_sc1_{}_overview'.format(group)] = self.make_summary_stat_df(series) # read in the marginal correlations vs. score for all data mcor_score_file = join(filedir, '{}_margcor_score_all_data.{}'.format(experiment_id, file_format)) if exists(mcor_score_file): files['df_mcor_sc1'] = DataReader.read_from_file(mcor_score_file, index_col=0) files['df_mcor_sc1_overview'] = self.make_summary_stat_df(files['df_mcor_sc1']) # read in the partial correlations by subgroups, if we are asked to for group in groups_eval: group_mcor_file = join(filedir, '{}_margcor_score_by_{}.{}'.format(experiment_id, group, file_format)) if exists(group_mcor_file): files['df_mcor_sc1_by_{}' ''.format(group)] = DataReader.read_from_file(group_mcor_file, index_col=0) series = files['df_mcor_sc1_by_{}'.format(group)] files['df_mcor_sc1_{}_overview'.format(group)] = self.make_summary_stat_df(series) pca_file = join(filedir, '{}_pca.{}'.format(experiment_id, file_format)) if exists(pca_file): files['df_pca'] = DataReader.read_from_file(pca_file, index_col=0) files['df_pcavar'] = DataReader.read_from_file(join(filedir, '{}_pcavar.{}'.format(experiment_id, file_format)), index_col=0) descriptives_file = join(filedir, '{}_feature_descriptives.{}'.format(experiment_id, file_format)) if exists(descriptives_file): # we read all files pertaining to the descriptive analysis together # since we merge the outputs files['df_descriptives'] = DataReader.read_from_file(descriptives_file, index_col=0) # this df contains only the number of features. this is used later # for another two tables to show the number of features df_features_n_values = files['df_descriptives'][['N', 'min', 'max']] files['df_descriptives'] = files['df_descriptives'][['N', 'mean', 'std. dev.', 'skewness', 'kurtosis']] outliers_file = join(filedir, '{}_feature_outliers.{}'.format(experiment_id, file_format)) df_outliers = DataReader.read_from_file(outliers_file, index_col=0) df_outliers = df_outliers.rename(columns={'upper': 'Upper', 'lower': 'Lower', 'both': 'Both', 'upperperc': 'Upper %', 'lowerperc': 'Lower %', 'bothperc': 'Both %'}) df_outliers_columns = df_outliers.columns.tolist() files['df_outliers'] = df_outliers # join with df_features_n_values to get the value of N files['df_outliers'] = pd.merge(files['df_outliers'], df_features_n_values, left_index=True, right_index=True)[['N'] + df_outliers_columns] # join with df_features_n_values to get the value of N percentiles_file = join(filedir, '{}_feature_descriptives' 'Extra.{}'.format(experiment_id, file_format)) files['df_percentiles'] = DataReader.read_from_file(percentiles_file, index_col=0) files['df_percentiles'] = pd.merge(files['df_percentiles'], df_features_n_values, left_index=True, right_index=True) mild_outliers = (files['df_percentiles']["Mild outliers"] / files['df_percentiles']["N"].astype(float) * 100) files['df_percentiles']["Mild outliers (%)"] = mild_outliers extreme_outliers = (files['df_percentiles']["Extreme outliers"] / files['df_percentiles']["N"].astype(float) * 100) files['df_percentiles']["Extreme outliers (%)"] = extreme_outliers files['df_percentiles'] = files['df_percentiles'][['N', 'min', 'max', '1%', '5%', '25%', '50%', '75%', '95%', '99%', 'IQR', 'Mild outliers', 'Mild outliers (%)', 'Extreme outliers', 'Extreme outliers (%)']] confmatrix_file = join(filedir, '{}_confMatrix.{}'.format(experiment_id, file_format)) if exists(confmatrix_file): conf_matrix = DataReader.read_from_file(confmatrix_file, index_col=0) files['df_confmatrix'] = self.process_confusion_matrix(conf_matrix) score_dist_file = join(filedir, '{}_score_dist.{}'.format(experiment_id, file_format)) if exists(score_dist_file): df_score_dist = DataReader.read_from_file(score_dist_file, index_col=1) df_score_dist.rename(columns={'sys_{}'.format(prefix): 'sys'}, inplace=True) files['df_score_dist'] = df_score_dist[['human', 'sys', 'difference']] # read in the feature boxplots by subgroup, if we were asked to for group in groups_eval: feature_boxplot_prefix = join(figdir, '{}_feature_boxplot_by_{}'.format(experiment_id, group)) svg_file = join(feature_boxplot_prefix + '.svg') png_file = join(feature_boxplot_prefix + '.png') if exists(svg_file): figs['feature_boxplots_by_{}_svg'.format(group)] = svg_file elif exists(png_file): figs['feature_boxplots_by_{}_png'.format(group)] = png_file # read in the betas image if exists betas_svg = join(figdir, '{}_betas.svg'.format(experiment_id)) if exists(betas_svg): figs['betas'] = betas_svg # read in the evaluation barplots by subgroup, if we were asked to for group in groups_eval: eval_barplot_svg_file = join(figdir, '{}_eval_by_{}.svg'.format(experiment_id, group)) if exists(eval_barplot_svg_file): figs['eval_barplot_by_{}'.format(group)] = eval_barplot_svg_file pca_svg_file = join(figdir, '{}_pca.svg'.format(experiment_id)) if exists(pca_svg_file): figs['pca_scree_plot'] = pca_svg_file return (files, figs, file_format)
def test_locate_files_str(self): paths = 'file1.csv' config_dir = 'output' result = DataReader.locate_files(paths, config_dir) eq_(result, None)
def compute_and_save_predictions(config_file_or_obj, output_file, feats_file=None): """ Run ``rsmpredict`` with given configuration file and generate predictions (and, optionally, pre-processed feature values). Parameters ---------- config_file_or_obj : str or configuration_parser.Configuration Path to the experiment configuration file. Users can also pass a `Configuration` object that is in memory. output_dir : str Path to the output directory for saving files. feats_file (optional): str Path to the output file for saving preprocessed feature values. Raises ------ ValueError If any of the required fields are missing or ill-specified. """ logger = logging.getLogger(__name__) # Allow users to pass Configuration object to the # `config_file_or_obj` argument, rather than read file if not isinstance(config_file_or_obj, Configuration): # Instantiate configuration parser object parser = ConfigurationParser.get_configparser(config_file_or_obj) config = parser.read_normalize_validate_and_process_config( config_file_or_obj, context='rsmpredict') # get the directory where the config file lives configpath = dirname(config_file_or_obj) else: config = config_file_or_obj if config.filepath is not None: configpath = dirname(config.filepath) else: configpath = os.getcwd() # get the experiment ID experiment_id = config['experiment_id'] # Get output format file_format = config.get('file_format', 'csv') # Get DataWriter object writer = DataWriter(experiment_id) # get the input file containing the feature values # for which we want to generate the predictions input_features_file = DataReader.locate_files( config['input_features_file'], configpath) if not input_features_file: raise FileNotFoundError('Input file {} does not exist' ''.format(config['input_features_file'])) experiment_dir = DataReader.locate_files(config['experiment_dir'], configpath) if not experiment_dir: raise FileNotFoundError('The directory {} does not exist.' ''.format(config['experiment_dir'])) else: experiment_output_dir = normpath(join(experiment_dir, 'output')) if not exists(experiment_output_dir): raise FileNotFoundError( 'The directory {} does not contain ' 'the output of an rsmtool experiment.'.format(experiment_dir)) # find all the .model files in the experiment output directory model_files = glob.glob(join(experiment_output_dir, '*.model')) if not model_files: raise FileNotFoundError( 'The directory {} does not contain any rsmtool models.' ''.format(experiment_output_dir)) experiment_ids = [splitext(basename(mf))[0] for mf in model_files] if experiment_id not in experiment_ids: raise FileNotFoundError( '{} does not contain a model for the experiment "{}". ' 'The following experiments are contained in this ' 'directory: {}'.format(experiment_output_dir, experiment_id, experiment_ids)) # check that the directory contains outher required files required_file_types = ['feature', 'postprocessing_params'] for file_type in required_file_types: expected_file_name = "{}_{}.csv".format(experiment_id, file_type) if not exists(join(experiment_output_dir, expected_file_name)): raise FileNotFoundError('{} does not contain the required file ' '{} that was generated during the ' 'original model training'.format( experiment_output_dir, expected_file_name)) # model_files = glob.glob(join(experiment_output_dir, '*.model')) # if not model_files: # raise FileNotFoundError('The directory {} does not contain any rsmtool models. ' # ''.format(experiment_output_dir)) logger.info('Reading input files.') feature_info = join(experiment_output_dir, '{}_feature.csv'.format(experiment_id)) post_processing = join( experiment_output_dir, '{}_postprocessing_params.csv'.format(experiment_id)) file_paths = [input_features_file, feature_info, post_processing] file_names = ['input_features', 'feature_info', 'postprocessing_params'] converters = {'input_features': config.get_default_converter()} # Initialize the reader reader = DataReader(file_paths, file_names, converters) data_container = reader.read( kwargs_dict={'feature_info': { 'index_col': 0 }}) # load the Modeler to generate the predictions model = Modeler.load_from_file( join(experiment_output_dir, '{}.model'.format(experiment_id))) # Add the model to the configuration object config['model'] = model # Initialize the processor processor = FeaturePreprocessor() (processed_config, processed_container) = processor.process_data(config, data_container, context='rsmpredict') # save the pre-processed features to disk if we were asked to if feats_file is not None: logger.info( 'Saving pre-processed feature values to {}'.format(feats_file)) feats_dir = dirname(feats_file) # create any directories needed for the output file os.makedirs(feats_dir, exist_ok=True) _, feats_filename = split(feats_file) feats_filename, _ = splitext(feats_filename) # Write out files writer.write_experiment_output( feats_dir, processed_container, include_experiment_id=False, dataframe_names=['features_processed'], new_names_dict={'features_processed': feats_filename}, file_format=file_format) if (output_file.lower().endswith('.csv') or output_file.lower().endswith('.xlsx')): output_dir = dirname(output_file) _, filename = split(output_file) filename, _ = splitext(filename) else: output_dir = output_file filename = 'predictions_with_metadata' # create any directories needed for the output file os.makedirs(output_dir, exist_ok=True) # save the predictions to disk logger.info('Saving predictions.') # Write out files writer.write_experiment_output( output_dir, processed_container, include_experiment_id=False, dataframe_names=['predictions_with_metadata'], new_names_dict={'predictions_with_metadata': filename}, file_format=file_format) # save excluded responses to disk if not processed_container.excluded.empty: # save the predictions to disk logger.info('Saving excluded responses to {}'.format( join(output_dir, '{}_excluded_responses.csv' ''.format(filename)))) # Write out files writer.write_experiment_output(output_dir, processed_container, include_experiment_id=False, dataframe_names=['excluded'], new_names_dict={ 'excluded': '{}_excluded_responses' ''.format(filename) }, file_format=file_format)
def check_subgroup_outputs(output_dir, experiment_id, subgroups, file_format='csv'): """ Check to make sure that the subgroup outputs look okay. Raise an AssertionError if they do not. Parameters ---------- output_dir : str Path to the `output` experiment output directory for a test. experiment_id : str The experiment ID. subgroups : list of str List of column names that contain grouping information. file_format : str, optional The format of the output files. Defaults to 'csv'. """ train_preprocessed_file = join( output_dir, '{}_train_metadata.{}'.format(experiment_id, file_format)) train_preprocessed = DataReader.read_from_file(train_preprocessed_file, index_col=0) test_preprocessed_file = join( output_dir, '{}_test_metadata.{}'.format(experiment_id, file_format)) test_preprocessed = DataReader.read_from_file(test_preprocessed_file, index_col=0) for group in subgroups: ok_(group in train_preprocessed.columns) ok_(group in test_preprocessed.columns) # check that the total sum of N per category matches the total N # in data composition and the total N categories matches what is # in overall data composition file_data_composition_all = join( output_dir, '{}_data_composition.{}'.format(experiment_id, file_format)) df_data_composition_all = DataReader.read_from_file( file_data_composition_all) for group in subgroups: file_composition_by_group = join( output_dir, '{}_data_composition_by_{}.{}'.format(experiment_id, group, file_format)) composition_by_group = DataReader.read_from_file( file_composition_by_group) for partition in ['Training', 'Evaluation']: partition_info = df_data_composition_all.loc[ df_data_composition_all['partition'] == partition] summation = sum(composition_by_group['{} set' ''.format(partition)]) ok_(summation == partition_info.iloc[0]['responses']) length = len(composition_by_group.loc[ composition_by_group['{} set' ''.format(partition)] != 0]) ok_(length == partition_info.iloc[0][group])
def test_locate_files_wrong_type(self): paths = {'file1.csv', 'file2.xlsx'} config_dir = 'output' DataReader.locate_files(paths, config_dir)