def main():
'''
Handles command line arguments and gets things started.
'''
parser = argparse.ArgumentParser(description="Prints out the weights of a \
given model.",
conflict_handler='resolve',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('model_file', help='model file to load')
parser.add_argument('--k',
help='number of top features to print (0 for all)',
type=int, default=50)
parser.add_argument('--version', action='version',
version='%(prog)s {0}'.format(__version__))
args = parser.parse_args()
# Make warnings from built-in warnings module get formatted more nicely
logging.captureWarnings(True)
logging.basicConfig(format=('%(asctime)s - %(name)s - %(levelname)s - ' +
'%(message)s'))
k = args.k if args.k > 0 else None
learner = Learner.from_file(args.model_file)
weights = learner.model_params
print("Number of nonzero features:", len(weights), file=sys.stderr)
for feat, val in sorted(iteritems(weights), key=lambda x: -abs(x[1]))[:k]:
print("{:.12f}\t{}".format(val, feat))
def main():
'''
Handles command line arguments and gets things started.
'''
parser = argparse.ArgumentParser(
description="Prints out the weights of a \
given model.",
conflict_handler='resolve',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('model_file', help='model file to load')
parser.add_argument('--k',
help='number of top features to print (0 for all)',
type=int,
default=50)
parser.add_argument('--version',
action='version',
version='%(prog)s {0}'.format(__version__))
args = parser.parse_args()
# Make warnings from built-in warnings module get formatted more nicely
logging.captureWarnings(True)
logging.basicConfig(format=('%(asctime)s - %(name)s - %(levelname)s - ' +
'%(message)s'))
k = args.k if args.k > 0 else None
learner = Learner.from_file(args.model_file)
weights = learner.model_params
print("Number of nonzero features:", len(weights), file=sys.stderr)
for feat, val in sorted(iteritems(weights), key=lambda x: -abs(x[1]))[:k]:
print("{:.12f}\t{}".format(val, feat))
def main(argv=None):
"""
Handles command line arguments and gets things started.
:param argv: List of arguments, as if specified on the command-line.
If None, ``sys.argv[1:]`` is used instead.
:type argv: list of str
"""
parser = argparse.ArgumentParser(description="Prints out the weights of a \
given model.",
conflict_handler='resolve',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('model_file', help='model file to load')
parser.add_argument('--k',
help='number of top features to print (0 for all)',
type=int, default=50)
parser.add_argument('--sign',
choices=['positive', 'negative', 'all'],
default='all',
help='show only positive, only negative, ' +
'or all weights')
parser.add_argument('--version', action='version',
version='%(prog)s {0}'.format(__version__))
args = parser.parse_args(argv)
# Make warnings from built-in warnings module get formatted more nicely
logging.captureWarnings(True)
logging.basicConfig(format=('%(asctime)s - %(name)s - %(levelname)s - ' +
'%(message)s'))
k = args.k if args.k > 0 else None
learner = Learner.from_file(args.model_file)
(weights, intercept) = learner.model_params
weight_items = iteritems(weights)
if args.sign == 'positive':
weight_items = (x for x in weight_items if x[1] > 0)
elif args.sign == 'negative':
weight_items = (x for x in weight_items if x[1] < 0)
if intercept is not None:
# subclass of LinearModel
if '_intercept_' in intercept:
# Some learners (e.g. LinearSVR) may return a list of intercepts
if isinstance(intercept['_intercept_'], np.ndarray):
intercept_list = ["%.12f" % i for i in intercept['_intercept_']]
print("intercept = {}".format(intercept_list))
else:
print("intercept = {:.12f}".format(intercept['_intercept_']))
else:
print("== intercept values ==")
for (label, val) in intercept.items():
print("{:.12f}\t{}".format(val, label))
print()
print("Number of nonzero features:", len(weights), file=sys.stderr)
for feat, val in sorted(weight_items, key=lambda x: -abs(x[1]))[:k]:
print("{:.12f}\t{}".format(val, feat))
def main(argv=None):
"""
Handles command line arguments and gets things started.
:param argv: List of arguments, as if specified on the command-line.
If None, ``sys.argv[1:]`` is used instead.
:type argv: list of str
"""
parser = argparse.ArgumentParser(
description="Prints out the weights of a \
given model.",
conflict_handler="resolve",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
parser.add_argument("model_file", help="model file to load")
parser.add_argument("--k", help="number of top features to print (0 for all)", type=int, default=50)
parser.add_argument(
"--sign",
choices=["positive", "negative", "all"],
default="all",
help="show only positive, only negative, " + "or all weights",
)
parser.add_argument("--version", action="version", version="%(prog)s {0}".format(__version__))
args = parser.parse_args(argv)
# Make warnings from built-in warnings module get formatted more nicely
logging.captureWarnings(True)
logging.basicConfig(format=("%(asctime)s - %(name)s - %(levelname)s - " + "%(message)s"))
k = args.k if args.k > 0 else None
learner = Learner.from_file(args.model_file)
(weights, intercept) = learner.model_params
weight_items = iteritems(weights)
if args.sign == "positive":
weight_items = (x for x in weight_items if x[1] > 0)
elif args.sign == "negative":
weight_items = (x for x in weight_items if x[1] < 0)
if intercept is not None:
# subclass of LinearModel
if "_intercept_" in intercept:
# Some learners (e.g. LinearSVR) may return a list of intercepts
if isinstance(intercept["_intercept_"], np.ndarray):
intercept_list = ["%.12f" % i for i in intercept["_intercept_"]]
print("intercept = {}".format(intercept_list))
else:
print("intercept = {:.12f}".format(intercept["_intercept_"]))
else:
print("== intercept values ==")
for (label, val) in intercept.items():
print("{:.12f}\t{}".format(val, label))
print()
print("Number of nonzero features:", len(weights), file=sys.stderr)
for feat, val in sorted(weight_items, key=lambda x: -abs(x[1]))[:k]:
print("{:.12f}\t{}".format(val, feat))
def main(argv=None):
"""
Handles command line arguments and gets things started.
:param argv: List of arguments, as if specified on the command-line.
If None, ``sys.argv[1:]`` is used instead.
:type argv: list of str
"""
parser = argparse.ArgumentParser(description="Prints out the weights of a \
given model.",
conflict_handler='resolve',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('model_file', help='model file to load')
parser.add_argument('--k',
help='number of top features to print (0 for all)',
type=int, default=50)
parser.add_argument('--sign',
choices=['positive', 'negative', 'all'],
default='all',
help='show only positive, only negative, ' +
'or all weights')
parser.add_argument('--version', action='version',
version='%(prog)s {0}'.format(__version__))
args = parser.parse_args(argv)
# Make warnings from built-in warnings module get formatted more nicely
logging.captureWarnings(True)
logging.basicConfig(format=('%(asctime)s - %(name)s - %(levelname)s - ' +
'%(message)s'))
k = args.k if args.k > 0 else None
learner = Learner.from_file(args.model_file)
(weights, intercept) = learner.model_params
weight_items = iteritems(weights)
if args.sign == 'positive':
weight_items = (x for x in weight_items if x[1] > 0)
elif args.sign == 'negative':
weight_items = (x for x in weight_items if x[1] < 0)
if intercept is not None:
# subclass of LinearModel
if '_intercept_' in intercept:
print("intercept = {:.12f}".format(intercept['_intercept_']))
else:
print("== intercept values ==")
for (label, val) in intercept.items():
print("{:.12f}\t{}".format(val, label))
print()
print("Number of nonzero features:", len(weights), file=sys.stderr)
for feat, val in sorted(weight_items, key=lambda x: -abs(x[1]))[:k]:
print("{:.12f}\t{}".format(val, feat))
def update_model(model_file):
"""Read in the model file and save it again."""
model_dir = dirname(model_file)
# get the list of current files so that we can
# remove them later to ensure there are no stranded
# .npy files
npy_files = glob.glob(join(model_dir, '*.npy'))
# now load the SKLL model
model = Learner.from_file(model_file)
# delete the existing npy files. The model file will get overwritten,
# but we do not know the exact number of current .npy files.
for npy_file in npy_files:
remove(npy_file)
model.save(model_file)
def update_model(model_file):
''' Read in the model file and save it again'''
model_dir = dirname(model_file)
# get the list of current files so that we can
# remove them later to ensure there are no stranded
# .npy files
npy_files = glob.glob(join(model_dir, '*.npy'))
# now load the SKLL model
model = Learner.from_file(model_file)
# delete the existing npy files. The model file will get overwritten,
# but we do not know the exact number of current .npy files.
for npy_file in npy_files:
remove(npy_file)
model.save(model_file)
def __init__(self, model_path, threshold=None, positive_class=1):
'''
Initialize the predictor.
:param model_path: Path to use when loading trained model.
:type model_path: str
:param threshold: If the model we're using is generating probabilities
of the positive class, return 1 if it meets/exceeds
the given threshold and 0 otherwise.
:type threshold: float
:param positive_class: If the model is only being used to predict the
probability of a particular class, this
specifies the index of the class we're
predicting. 1 = second class, which is default
for binary classification.
:type positive_class: int
'''
self._learner = Learner.from_file(model_path)
self._pos_index = positive_class
self.threshold = threshold
def __init__(self, model_path, threshold=None, positive_class=1):
'''
Initialize the predictor.
:param model_path: Path to use when loading trained model.
:type model_path: str
:param threshold: If the model we're using is generating probabilities
of the positive class, return 1 if it meets/exceeds
the given threshold and 0 otherwise.
:type threshold: float
:param positive_class: If the model is only being used to predict the
probability of a particular class, this
specifies the index of the class we're
predicting. 1 = second class, which is default
for binary classification.
:type positive_class: int
'''
self._learner = Learner.from_file(model_path)
self._pos_index = positive_class
self.threshold = threshold
def compute_and_save_predictions(config_file, output_file, feats_file):
"""
Generate predictions using the information in the config file
and save them into the given output file.
"""
logger = logging.getLogger(__name__)
# read in the main config file
config_obj = read_json_file(config_file)
config_obj = check_main_config(config_obj, context='rsmpredict')
# get the directory where the config file lives
# if this is the 'expm' directory, then go
# up one level.
configpath = dirname(config_file)
# get the input file containing the feature values
# for which we want to generate the predictions
input_features_file = locate_file(config_obj['input_features_file'], configpath)
if not input_features_file:
raise FileNotFoundError('Input file {} does not exist'.format(config_obj['input_features_file']))
# get the experiment ID
experiment_id = config_obj['experiment_id']
# get the column name that will hold the ID
id_column = config_obj['id_column']
# get the column name for human score (if any)
human_score_column = config_obj['human_score_column']
# get the column name for second human score (if any)
second_human_score_column = config_obj['second_human_score_column']
# get the column name for subgroups (if any)
subgroups = config_obj['subgroups']
# get the column names for flag columns (if any)
flag_column_dict = check_flag_column(config_obj)
# get the name for the candidate_column (if any)
candidate_column = config_obj['candidate_column']
# get the directory of the experiment
experiment_dir = locate_file(config_obj['experiment_dir'], configpath)
if not experiment_dir:
raise FileNotFoundError('The directory {} does not exist.'.format(config_obj['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))
# read in the given features but make sure that the
# `id_column`, `candidate_column` and subgroups are read in as a string
logger.info('Reading features from {}'.format(input_features_file))
string_columns = [id_column, candidate_column] + subgroups
converter_dict = dict([(column, str) for column in string_columns if column])
df_input = pd.read_csv(input_features_file, converters=converter_dict)
# make sure that the columns specified in the config file actually exist
columns_to_check = [id_column] + subgroups + list(flag_column_dict.keys())
# add subgroups and the flag columns to the list of columns
# that will be added to the final file
columns_to_copy = subgroups + list(flag_column_dict.keys())
# human_score_column will be set to sc1 by default
# we only raise an error if it's set to something else.
# However, since we cannot distinguish whether the column was set
# to sc1 by default or specified as such in the config file
# we append it to output anyway as long as
# it is in the input file
if human_score_column != 'sc1' or 'sc1' in df_input.columns:
columns_to_check.append(human_score_column)
columns_to_copy.append('sc1')
if candidate_column:
columns_to_check.append(candidate_column)
columns_to_copy.append('candidate')
if second_human_score_column:
columns_to_check.append(second_human_score_column)
columns_to_copy.append('sc2')
missing_columns = set(columns_to_check).difference(df_input.columns)
if missing_columns:
raise KeyError("Columns {} from the config file "
"do not exist in the data.".format(missing_columns))
# rename all columns
df_input = rename_default_columns(df_input,
[],
id_column,
human_score_column,
second_human_score_column,
None,
None,
candidate_column=candidate_column)
# check that the id_column contains unique values
if df_input['spkitemid'].size != df_input['spkitemid'].unique().size:
raise ValueError("The data contains repeated response IDs in {}. Please make sure all response IDs are unique and re-run the tool.".format(id_column))
# now we need to pre-process these features using
# the parameters that are already stored in the
# _features.csv file.
df_feature_info = pd.read_csv(join(experiment_output_dir,
'{}_feature.csv'.format(experiment_id)),
index_col=0)
required_features = df_feature_info.index.tolist()
# ensure that all the features that are needed by the model
# are present in the input file
input_feature_columns = [c for c in df_input if c != id_column]
missing_features = set(required_features).difference(input_feature_columns)
if missing_features:
raise KeyError('{} is missing the following features: {}'.format(feats_file, missing_features))
extra_features = set(input_feature_columns).difference(required_features + [id_column])
if extra_features:
logging.warning('The following extraenous features will be ignored: {}'.format(extra_features))
# keep the required features plus the id
features_to_keep = ['spkitemid'] + required_features
# check if actually have the human scores for this data and add
# sc1 to preprocessed features for consistency with other tools
has_human_scores = 'sc1' in df_input
if has_human_scores:
features_to_keep.append('sc1')
df_features = df_input[features_to_keep]
# preprocess the feature values
logger.info('Pre-processing input features')
# first we need to filter out NaNs and any other
# weird features, the same way we did for rsmtool.
df_filtered = df_features.copy()
df_excluded = pd.DataFrame(columns=df_filtered.columns)
for feature_name in required_features:
newdf, newdf_excluded = filter_on_column(df_filtered, feature_name, 'spkitemid',
exclude_zeros=False,
exclude_zero_sd=False)
del df_filtered
df_filtered = newdf
df_excluded = pd.merge(df_excluded, newdf_excluded, how='outer')
# make sure that the remaining data frame is not empty
if len(df_filtered) == 0:
raise ValueError("There are no responses left after "
"filtering out non-numeric feature values. No analysis "
"will be run")
df_features = df_filtered.copy()
df_features_preprocessed = df_features.copy()
for feature_name in required_features:
feature_values = df_features[feature_name].values
feature_transformation = df_feature_info.loc[feature_name]['transform']
feature_weight = df_feature_info.loc[feature_name]['sign']
train_feature_mean = df_feature_info.loc[feature_name]['train_mean']
train_feature_sd = df_feature_info.loc[feature_name]['train_sd']
train_transformed_mean = df_feature_info.loc[feature_name]['train_transformed_mean']
train_transformed_sd = df_feature_info.loc[feature_name]['train_transformed_sd']
# transform the feature values and remove outliers
df_features_preprocessed[feature_name] = preprocess_feature(feature_values,
feature_name,
feature_transformation,
train_feature_mean,
train_feature_sd,
exclude_zero_sd=False)
# now standardize the feature values
df_features_preprocessed[feature_name] = (df_features_preprocessed[feature_name] - train_transformed_mean) / train_transformed_sd
# Multiply features by weight. Within the
# current SR timeline, the mean of the transformed train
# feature used to standardize test features has to be
# computed before multiplying the train feature by the weight.
df_features_preprocessed[feature_name] = df_features_preprocessed[feature_name] * feature_weight
# save the pre-processed features to disk if we were asked to
if feats_file:
logger.info('Saving pre-processed feature values to {}'.format(feats_file))
# create any directories needed for the output file
os.makedirs(dirname(feats_file), exist_ok=True)
df_features_preprocessed.to_csv(feats_file, index=False)
# now load the SKLL model to generate the predictions
model = Learner.from_file(join(experiment_output_dir, '{}.model'.format(experiment_id)))
# now generate the predictions for the features using this model
logger.info('Generating predictions')
df_predictions = predict_with_model(model, df_features_preprocessed)
# read in the post-processing parameters from disk
df_postproc_params = pd.read_csv(join(experiment_output_dir, '{}_postprocessing_params.csv'.format(experiment_id)))
trim_min = df_postproc_params['trim_min'].values[0]
trim_max = df_postproc_params['trim_max'].values[0]
h1_mean = df_postproc_params['h1_mean'].values[0]
h1_sd = df_postproc_params['h1_sd'].values[0]
train_predictions_mean = df_postproc_params['train_predictions_mean'].values[0]
train_predictions_sd = df_postproc_params['train_predictions_sd'].values[0]
# now scale the predictions
logger.info('Rescaling predictions')
scaled_predictions = (df_predictions['raw'] - train_predictions_mean) / train_predictions_sd
scaled_predictions = scaled_predictions * h1_sd + h1_mean
df_predictions['scale'] = scaled_predictions
# trim and round the predictions
logger.info('Trimming and rounding predictions')
df_predictions['raw_trim'] = trim(df_predictions['raw'], trim_min, trim_max)
df_predictions['raw_trim_round'] = np.rint(df_predictions['raw_trim']).astype('int64')
df_predictions['scale_trim'] = trim(df_predictions['scale'], trim_min, trim_max)
df_predictions['scale_trim_round'] = np.rint(df_predictions['scale_trim']).astype('int64')
# add back the columns that we were requested to copy if any
if columns_to_copy:
df_predictions_with_metadata = pd.merge(df_predictions,
df_input[['spkitemid'] + columns_to_copy])
assert(len(df_predictions) == len(df_predictions_with_metadata))
else:
df_predictions_with_metadata = df_predictions.copy()
# create any directories needed for the output file
os.makedirs(dirname(output_file), exist_ok=True)
# save the predictions to disk
logger.info('Saving predictions to {}'.format(output_file))
df_predictions_with_metadata.to_csv(output_file, index=False)
# save excluded responses to disk
if not df_excluded.empty:
excluded_output_file = '{}_excluded_responses{}'.format(*splitext(output_file))
logger.info('Saving excluded responses to {}'.format(excluded_output_file))
df_excluded.to_csv(excluded_output_file, index=False)
def main(argv=None):
"""
Handles command line arguments and gets things started.
Parameters
----------
argv : list of str
List of arguments, as if specified on the command-line.
If None, ``sys.argv[1:]`` is used instead.
"""
parser = argparse.ArgumentParser(
description="Prints out the weights of a \
given model.",
conflict_handler='resolve',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('model_file', help='model file to load')
group = parser.add_mutually_exclusive_group()
group.add_argument('--k',
help='number of top features to print (0 for all)',
type=int,
default=50)
group.add_argument("--sort_by_labels",
'-s',
action='store_true',
default=False,
help="order the features by classes")
parser.add_argument(
'--sign',
choices=['positive', 'negative', 'all'],
default='all',
help='show only positive, only negative or all weights')
parser.add_argument('--version',
action='version',
version='%(prog)s {0}'.format(__version__))
args = parser.parse_args(argv)
# Make warnings from built-in warnings module get formatted more nicely
logging.captureWarnings(True)
logging.basicConfig(format=('%(asctime)s - %(name)s - %(levelname)s - '
'%(message)s'))
k = args.k if args.k > 0 else None
learner = Learner.from_file(args.model_file)
(weights, intercept) = learner.model_params
multiclass = False
model = learner._model
if (isinstance(model, LinearSVC) or
(isinstance(model, LogisticRegression) and len(learner.label_list) > 2)
or (isinstance(model, SVC) and model.kernel == 'linear')):
multiclass = True
weight_items = weights.items()
if args.sign == 'positive':
weight_items = (x for x in weight_items if x[1] > 0)
elif args.sign == 'negative':
weight_items = (x for x in weight_items if x[1] < 0)
if intercept is not None:
# subclass of LinearModel
if '_intercept_' in intercept:
# Some learners (e.g. LinearSVR) may return an array of intercepts but
# sometimes that array is of length 1 so we don't need to print that
# as an array/list. First, let's normalize these cases.
model_intercepts = intercept['_intercept_']
intercept_is_array = isinstance(model_intercepts, np.ndarray)
num_intercepts = len(model_intercepts) if intercept_is_array else 1
if intercept_is_array and num_intercepts == 1:
model_intercepts = model_intercepts[0]
intercept_is_array = False
# now print out the intercepts
print("intercept = {:.12f}".format(model_intercepts))
else:
print("== intercept values ==")
for (label, val) in intercept.items():
print("{: .12f}\t{}".format(val, label))
print()
print("Number of nonzero features:", len(weights), file=sys.stderr)
weight_by_class = defaultdict(dict)
if multiclass and args.sort_by_labels:
for label_feature, weight in weight_items:
label, feature = label_feature.split()
weight_by_class[label][feature] = weight
for label in sorted(weight_by_class):
for feat, val in sorted(weight_by_class[label].items(),
key=lambda x: -abs(x[1])):
print("{: .12f}\t{}\t{}".format(val, label, feat))
else:
for feat, val in sorted(weight_items, key=lambda x: -abs(x[1]))[:k]:
print("{: .12f}\t{}".format(val, feat))
def compute_and_save_predictions(config_file, output_file, feats_file):
"""
Generate predictions using the information in the config file
and save them into the given output file.
"""
logger = logging.getLogger(__name__)
# read in the main config file
config_obj = read_json_file(config_file)
config_obj = check_main_config(config_obj, context='rsmpredict')
# get the directory where the config file lives
# if this is the 'expm' directory, then go
# up one level.
configpath = dirname(config_file)
# get the input file containing the feature values
# for which we want to generate the predictions
input_features_file = locate_file(config_obj['input_features_file'],
configpath)
if not input_features_file:
raise FileNotFoundError('Input file {} does not exist'.format(
config_obj['input_features_file']))
# get the experiment ID
experiment_id = config_obj['experiment_id']
# get the column name that will hold the ID
id_column = config_obj['id_column']
# get the column name for human score (if any)
human_score_column = config_obj['human_score_column']
# get the column name for second human score (if any)
second_human_score_column = config_obj['second_human_score_column']
# get the column name for subgroups (if any)
subgroups = config_obj['subgroups']
# get the column names for flag columns (if any)
flag_column_dict = check_flag_column(config_obj)
# get the name for the candidate_column (if any)
candidate_column = config_obj['candidate_column']
# get the directory of the experiment
experiment_dir = locate_file(config_obj['experiment_dir'], configpath)
if not experiment_dir:
raise FileNotFoundError('The directory {} does not exist.'.format(
config_obj['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))
# read in the given features but make sure that the
# `id_column`, `candidate_column` and subgroups are read in as a string
logger.info('Reading features from {}'.format(input_features_file))
string_columns = [id_column, candidate_column] + subgroups
converter_dict = dict([(column, str) for column in string_columns
if column])
df_input = pd.read_csv(input_features_file, converters=converter_dict)
# make sure that the columns specified in the config file actually exist
columns_to_check = [id_column] + subgroups + list(flag_column_dict.keys())
# add subgroups and the flag columns to the list of columns
# that will be added to the final file
columns_to_copy = subgroups + list(flag_column_dict.keys())
# human_score_column will be set to sc1 by default
# we only raise an error if it's set to something else.
# However, since we cannot distinguish whether the column was set
# to sc1 by default or specified as such in the config file
# we append it to output anyway as long as
# it is in the input file
if human_score_column != 'sc1' or 'sc1' in df_input.columns:
columns_to_check.append(human_score_column)
columns_to_copy.append('sc1')
if candidate_column:
columns_to_check.append(candidate_column)
columns_to_copy.append('candidate')
if second_human_score_column:
columns_to_check.append(second_human_score_column)
columns_to_copy.append('sc2')
missing_columns = set(columns_to_check).difference(df_input.columns)
if missing_columns:
raise KeyError("Columns {} from the config file "
"do not exist in the data.".format(missing_columns))
# rename all columns
df_input = rename_default_columns(df_input, [],
id_column,
human_score_column,
second_human_score_column,
None,
None,
candidate_column=candidate_column)
# check that the id_column contains unique values
if df_input['spkitemid'].size != df_input['spkitemid'].unique().size:
raise ValueError(
"The data contains repeated response IDs in {}. Please make sure all response IDs are unique and re-run the tool."
.format(id_column))
# now we need to pre-process these features using
# the parameters that are already stored in the
# _features.csv file.
df_feature_info = pd.read_csv(join(experiment_output_dir,
'{}_feature.csv'.format(experiment_id)),
index_col=0)
required_features = df_feature_info.index.tolist()
# ensure that all the features that are needed by the model
# are present in the input file
input_feature_columns = [c for c in df_input if c != id_column]
missing_features = set(required_features).difference(input_feature_columns)
if missing_features:
raise KeyError('{} is missing the following features: {}'.format(
feats_file, missing_features))
extra_features = set(input_feature_columns).difference(required_features +
[id_column])
if extra_features:
logging.warning(
'The following extraenous features will be ignored: {}'.format(
extra_features))
# keep the required features plus the id
features_to_keep = ['spkitemid'] + required_features
# check if actually have the human scores for this data and add
# sc1 to preprocessed features for consistency with other tools
has_human_scores = 'sc1' in df_input
if has_human_scores:
features_to_keep.append('sc1')
df_features = df_input[features_to_keep]
# preprocess the feature values
logger.info('Pre-processing input features')
# first we need to filter out NaNs and any other
# weird features, the same way we did for rsmtool.
df_filtered = df_features.copy()
df_excluded = pd.DataFrame(columns=df_filtered.columns)
for feature_name in required_features:
newdf, newdf_excluded = filter_on_column(df_filtered,
feature_name,
'spkitemid',
exclude_zeros=False,
exclude_zero_sd=False)
del df_filtered
df_filtered = newdf
df_excluded = pd.merge(df_excluded, newdf_excluded, how='outer')
# make sure that the remaining data frame is not empty
if len(df_filtered) == 0:
raise ValueError(
"There are no responses left after "
"filtering out non-numeric feature values. No analysis "
"will be run")
df_features = df_filtered.copy()
df_features_preprocessed = df_features.copy()
for feature_name in required_features:
feature_values = df_features[feature_name].values
feature_transformation = df_feature_info.loc[feature_name]['transform']
feature_weight = df_feature_info.loc[feature_name]['sign']
train_feature_mean = df_feature_info.loc[feature_name]['train_mean']
train_feature_sd = df_feature_info.loc[feature_name]['train_sd']
train_transformed_mean = df_feature_info.loc[feature_name][
'train_transformed_mean']
train_transformed_sd = df_feature_info.loc[feature_name][
'train_transformed_sd']
# transform the feature values and remove outliers
df_features_preprocessed[feature_name] = preprocess_feature(
feature_values,
feature_name,
feature_transformation,
train_feature_mean,
train_feature_sd,
exclude_zero_sd=False)
# now standardize the feature values
df_features_preprocessed[feature_name] = (
df_features_preprocessed[feature_name] -
train_transformed_mean) / train_transformed_sd
# Multiply features by weight. Within the
# current SR timeline, the mean of the transformed train
# feature used to standardize test features has to be
# computed before multiplying the train feature by the weight.
df_features_preprocessed[feature_name] = df_features_preprocessed[
feature_name] * feature_weight
# save the pre-processed features to disk if we were asked to
if feats_file:
logger.info(
'Saving pre-processed feature values to {}'.format(feats_file))
# create any directories needed for the output file
os.makedirs(dirname(feats_file), exist_ok=True)
df_features_preprocessed.to_csv(feats_file, index=False)
# now load the SKLL model to generate the predictions
model = Learner.from_file(
join(experiment_output_dir, '{}.model'.format(experiment_id)))
# now generate the predictions for the features using this model
logger.info('Generating predictions')
df_predictions = predict_with_model(model, df_features_preprocessed)
# read in the post-processing parameters from disk
df_postproc_params = pd.read_csv(
join(experiment_output_dir,
'{}_postprocessing_params.csv'.format(experiment_id)))
trim_min = df_postproc_params['trim_min'].values[0]
trim_max = df_postproc_params['trim_max'].values[0]
h1_mean = df_postproc_params['h1_mean'].values[0]
h1_sd = df_postproc_params['h1_sd'].values[0]
train_predictions_mean = df_postproc_params[
'train_predictions_mean'].values[0]
train_predictions_sd = df_postproc_params['train_predictions_sd'].values[0]
# now scale the predictions
logger.info('Rescaling predictions')
scaled_predictions = (df_predictions['raw'] -
train_predictions_mean) / train_predictions_sd
scaled_predictions = scaled_predictions * h1_sd + h1_mean
df_predictions['scale'] = scaled_predictions
# trim and round the predictions
logger.info('Trimming and rounding predictions')
df_predictions['raw_trim'] = trim(df_predictions['raw'], trim_min,
trim_max)
df_predictions['raw_trim_round'] = np.rint(
df_predictions['raw_trim']).astype('int64')
df_predictions['scale_trim'] = trim(df_predictions['scale'], trim_min,
trim_max)
df_predictions['scale_trim_round'] = np.rint(
df_predictions['scale_trim']).astype('int64')
# add back the columns that we were requested to copy if any
if columns_to_copy:
df_predictions_with_metadata = pd.merge(
df_predictions, df_input[['spkitemid'] + columns_to_copy])
assert (len(df_predictions) == len(df_predictions_with_metadata))
else:
df_predictions_with_metadata = df_predictions.copy()
# create any directories needed for the output file
os.makedirs(dirname(output_file), exist_ok=True)
# save the predictions to disk
logger.info('Saving predictions to {}'.format(output_file))
df_predictions_with_metadata.to_csv(output_file, index=False)
# save excluded responses to disk
if not df_excluded.empty:
excluded_output_file = '{}_excluded_responses{}'.format(
*splitext(output_file))
logger.info(
'Saving excluded responses to {}'.format(excluded_output_file))
df_excluded.to_csv(excluded_output_file, index=False)
def main(argv=None):
"""
Handles command line arguments and gets things started.
Parameters
----------
argv : list of str
List of arguments, as if specified on the command-line.
If None, ``sys.argv[1:]`` is used instead.
"""
parser = argparse.ArgumentParser(description="Prints out the weights of a \
given model.",
conflict_handler='resolve',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('model_file', help='model file to load')
group = parser.add_mutually_exclusive_group()
group.add_argument('--k',
help='number of top features to print (0 for all)',
type=int, default=50)
group.add_argument("--sort_by_labels", '-s', action='store_true',
default=False, help="order the features by classes")
parser.add_argument('--sign',
choices=['positive', 'negative', 'all'],
default='all',
help='show only positive, only negative, ' +
'or all weights')
parser.add_argument('--version', action='version',
version='%(prog)s {0}'.format(__version__))
args = parser.parse_args(argv)
# Make warnings from built-in warnings module get formatted more nicely
logging.captureWarnings(True)
logging.basicConfig(format=('%(asctime)s - %(name)s - %(levelname)s - ' +
'%(message)s'))
k = args.k if args.k > 0 else None
learner = Learner.from_file(args.model_file)
(weights, intercept) = learner.model_params
multiclass = False
model = learner._model
if (isinstance(model, LinearSVC) or
(isinstance(model, LogisticRegression) and
len(learner.label_list) > 2) or
(isinstance(model, SVC) and
model.kernel == 'linear')):
multiclass = True
weight_items = iteritems(weights)
if args.sign == 'positive':
weight_items = (x for x in weight_items if x[1] > 0)
elif args.sign == 'negative':
weight_items = (x for x in weight_items if x[1] < 0)
if intercept is not None:
# subclass of LinearModel
if '_intercept_' in intercept:
# Some learners (e.g. LinearSVR) may return an array of intercepts but
# sometimes that array is of length 1 so we don't need to print that
# as an array/list. First, let's normalize these cases.
model_intercepts = intercept['_intercept_']
intercept_is_array = isinstance(model_intercepts, np.ndarray)
num_intercepts = len(model_intercepts) if intercept_is_array else 1
if intercept_is_array and num_intercepts == 1:
model_intercepts = model_intercepts[0]
intercept_is_array = False
# now print out the intercepts
print("intercept = {:.12f}".format(model_intercepts))
else:
print("== intercept values ==")
for (label, val) in intercept.items():
print("{: .12f}\t{}".format(val, label))
print()
print("Number of nonzero features:", len(weights), file=sys.stderr)
weight_by_class = defaultdict(dict)
if multiclass and args.sort_by_labels:
for label_feature, weight in weight_items:
label, feature = label_feature.split()
weight_by_class[label][feature] = weight
for label in sorted(weight_by_class):
for feat, val in sorted(weight_by_class[label].items(), key=lambda x: -abs(x[1])):
print("{: .12f}\t{}\t{}".format(val, label, feat))
else:
for feat, val in sorted(weight_items, key=lambda x: -abs(x[1]))[:k]:
print("{: .12f}\t{}".format(val, feat))
