def setUp(self):
data_path = (os.path.join(os.path.dirname(__file__),
"test_participant.json"))
TFIDF = False
N_GRAMS = (1, 2)
self.data = TrainData.load(data_path)
train_ids = [t.ids for t in self.data.data["1.ABC"]]
flat_train_ids = list(self.data.data["1.ABC"].ids)
self.train_cntxt, self.train_acts = self._format_cntxt_indices(
train_ids)
self.X_speech, vectorizer = get_bow_features(self.data,
tfidf=TFIDF,
n_grams=N_GRAMS,
max_features=None)
self.X_speech = self.X_speech[flat_train_ids, :]
model_gen = JointModel.model_generator(SGDClassifier,
loss='log',
average=True,
penalty='l2',
alpha=0.0002)
self.combined_model = CombinedModel(vectorizer,
model_gen,
ALL_ACTIONS,
speech_eps=0.15,
context_eps=0.15)
self.test_utter = "The green piece with two black stripes"
self.test_cntxt = []
def plot_trial(trial, bag):
model_path = os.path.join(args.model_path, args.participant, str(trial),
"model_initial")
fig_path = os.path.join(
os.path.dirname(__file__), "figs", args.participant, str(trial))
if not os.path.exists(fig_path):
os.makedirs(fig_path)
model = CombinedModel.load_from_path(model_path, ALL_ACTIONS,
JointModel.model_generator(
SGDClassifier,
**SPEECH_MODEL_PARAMETERS),
SPEECH_EPS, CONTEXT_EPS)
row = 4
col = 6
cntxt = []
i = 0
for m in bag.read_messages():
if m.topic == TOPIC:
model.predict(cntxt, m.message.utter, plot=True)
cntxt.append(m.message.action)
i += 1
plt.tight_layout()
path = os.path.join(fig_path, "sample_{}_{}".format(
m.message.result, i))
plt.savefig(path, format="svg")
plt.clf()
def train_combined_model(speech_eps,
context_eps,
fit_type="incremental",
tfidf=False,
n_grams=(1, 2),
speech_model_class=JointModel,
speech_model_parameters={},
init_new_speech_actions=False):
path = defaults.DATA_PATH
print("PATH: ", os.path.join(path, "train.json"))
data = TrainData.load(os.path.join(path, "train.json"))
flat_train_ids = [i for p in TRAIN_PARTICIPANTS for i in data.data[p].ids]
train_ids_by_trial = [
trial.ids for p in TRAIN_PARTICIPANTS for trial in data.data[p]
]
# Get features
train_context, labels = format_cntxt_indices(data, train_ids_by_trial)
X_speech, vectorizer = get_bow_features(data,
tfidf=tfidf,
n_grams=n_grams,
max_features=None)
X_speech = X_speech[flat_train_ids, :]
model_gen = JointModel.model_generator(speech_model_class,
**speech_model_parameters)
combined_model = CombinedModel(vectorizer,
model_gen,
ALL_ACTIONS,
speech_eps=speech_eps,
context_eps=context_eps)
if "incremental" in fit_type:
combined_model.partial_fit(train_context, X_speech, labels)
elif "offline" in fit_type:
combined_model.fit(train_context, X_speech, labels)
if init_new_speech_actions:
if "incremental" not in fit_type:
raise NotImplementedError(
"Can't add speech data on offline speech")
update_speech_for_new_actions(combined_model.speech_model,
combined_model._vectorizer,
weight=len(labels) * 1. /
len(ALL_ACTIONS))
return combined_model
def _get_model(self, participant, trial):
if trial == 0:
model_type = "model_initial"
t = 0
else:
model_type = "model_final"
t = trial - 1
model_path = os.path.join(args.model_path, participant, str(t + 1),
model_type)
return CombinedModel.load_from_path(
model_path, ALL_ACTIONS,
JointModel.model_generator(SGDClassifier,
**SPEECH_MODEL_PARAMETERS), SPEECH_EPS,
CONTEXT_EPS)
#!/usr/bin/env python
from sklearn.linear_model import SGDClassifier
from matplotlib import pyplot as plt
from hrc_speech_prediction.models import (
JointModel, get_path_from_cli_arguments)
from hrc_speech_prediction.evaluation import Evaluation
from hrc_speech_prediction.plots import plot_incremental_scores
N_GRAMS = (1, 2)
TFIDF = False
working_path = get_path_from_cli_arguments()
speech_model_gen = JointModel.model_generator(
SGDClassifier,
loss='log', average=True, penalty='l2', alpha=.0002)
ev = Evaluation(speech_model_gen, working_path, n_grams=N_GRAMS, tfidf=TFIDF,
model_variations={k: {'features': k}
for k in ['speech', 'context', 'both']})
ev.evaluate_all()
scores = ev.evaluate_incremental_learning(shuffle=False)
for m in scores:
plot_incremental_scores(scores[m], label=m)
plt.legend()
plt.show()
def _load_model(self, model_path, speech_eps, context_eps):
self.model = CM.load_from_path(model_path, ALL_ACTIONS,
JointModel.model_generator(
SGDClassifier,
**SPEECH_MODEL_PARAMETERS),
speech_eps, context_eps)
TFIDF = False
MODEL_PARAMS = {
'loss': 'log',
'average': True,
'penalty': 'l2',
'alpha': .02,
'max_iter': 100,
'tol': 1.e-3,
}
working_path = get_path_from_cli_arguments()
fig_path = os.path.join(working_path, 'figs')
if not os.path.isdir(fig_path):
os.mkdir(fig_path)
speech_model_gen = JointModel.model_generator(SGDClassifier, **MODEL_PARAMS)
ev = Evaluation(speech_model_gen, working_path, n_grams=N_GRAMS, tfidf=TFIDF,
model_variations={'speech': {'features': 'speech'}})
ev.evaluate_all()
classes = list(set(ev.data.labels))
utterances = list(ev.data.utterances)
digits = int(np.ceil(np.math.log10(len(utterances))))
plt.set_cmap('Blues')
fig = plt.figure()
for tst in TRAIN_PARTICIPANTS:
train_idx = [i for p in TRAIN_PARTICIPANTS
for i in list(ev.data.data[p].ids)
if not p == tst]
X_train = ev.get_Xs(train_idx)