def pos_word_by_ml(self, awords):
env = Environment()
enc = Word_Encoder()
file_model = env.filename_model_tree()
clf = pickle.load(open(file_model, 'rb'))
a_predict = np.array([enc.word2token('')])
for word in awords:
a_padd = [enc.word2token(word)]
#print(word, a_padd)
a_predict = np.append(a_predict, a_padd, axis=0)
a_predict = a_predict[1:]
#print(a_predict[0, 100])
predictions = clf.predict(a_predict[:, 0:])
return (predictions[0:])
def pos(self, df, mode_fast=True, use_cache=True):
env = Environment()
enc = Word_Encoder()
df_res = df
t_start = timer()
c = OpenCorpus()
g = c.grammemes()
dg = g.to_dict().get('name')
#Cache file
cache_columns = ['word', 'gram_ml', 'count']
file_cache = env.filename_mlcache_csv()
try:
df_cache = pd.read_csv(file_cache,
index_col='idcorpus',
encoding='utf-8')
except:
env.debug(
1,
['POSTagger', 'pos', 'Failed to read cache file:', file_cache])
df_cache = pd.DataFrame(columns=cache_columns)
else:
env.debug(1, ['POSTagger', 'pos', 'Read ML cache OK:', file_cache])
a_predict = np.array([enc.word2token('')])
#a_words = ['']
n_words = df_res.shape[0]
env.debug(1, [
'POStagger', 'pos',
'START Vocabulary prediction %s words' % n_words
])
a_words = df_res['word'].tolist()
a_ml_words = []
predictions_voc = self.pos_by_voc(a_words)
p_se = pd.Series(predictions_voc)
df_res['gram'] = p_se.values
df_res['gram_voc'] = p_se.values
df_res['gram_ml'] = ''
t_end = timer()
env.debug(1, [
'POStagger', 'pos',
'END Vocabulary prediction %s sec.' % env.job_time(t_start, t_end)
])
#print(predictions_voc)
if mode_fast:
#env.debug(1, ['POStagger', 'pos', 'START Fast mode vocabulary search. Words %s' % df.shape[0]])
df_ni_voc = df_res[df_res['gram_voc'] == '']
n_words = df_ni_voc.shape[0]
else:
df_ni_voc = df_res
#print('non-vocabulary',df_ni_voc)
if not df_ni_voc.empty:
env.debug(
1, ['POStagger', 'pos',
'START Encoding %s words' % n_words])
for index, serie in df_ni_voc.iterrows():
word = df_ni_voc.at[index, 'word']
#print(word)
a_padd = np.array([enc.word2token(word)])
a_predict = np.append(a_predict, a_padd, axis=0)
a_ml_words.append(word)
#print(a_words, a_predict)
a_predict = a_predict[1:, :]
#print(a_predict)
#print('ml_words',a_ml_words)
t_end = timer()
env.debug(1, [
'POStagger', 'pos',
'END Encoding %s words %s sec.' %
(n_words, env.job_time(t_start, t_end))
])
t_start = timer()
env.debug(1, ['POStagger', 'pos', 'START Model prediction'])
clf = pickle.load(open(env.filename_model_tree(), 'rb'))
predictions_ml = clf.predict(a_predict[:, 0:])
# print('ml', predictions_ml)
t_end = timer()
env.debug(1, [
'POStagger', 'pos',
'END Model prediction %s sec.' % env.job_time(t_start, t_end)
])
#print('ml_words_prediction',list(zip(a_ml_words,predictions_ml)))
t_start = timer()
i = 0
s_pvoc = ''
s_pml = ''
for index, row in df_res.iterrows():
word = df_res.at[index, 'word']
s_pvoc = df_res.at[index, 'gram_voc']
#s_pvoc = predictions_voc[i]
#print('s_pvoc', word, s_pvoc)
#df_res.at[index, 'gram_voc'] = s_pvoc
if s_pvoc == '':
if mode_fast:
try:
j = a_ml_words.index(word)
except:
pass
else:
s_pml = dg.get(predictions_ml[j])
#print(word,s_pml)
else:
s_pml = dg.get(predictions_ml[i])
df_res.at[index, 'gram_ml'] = s_pml
df_res.at[index, 'gram'] = s_pml
i = i + 1
t_end = timer()
env.debug(1, [
'POStagger', 'pos',
'ML predictions dataframe filled %s sec' %
env.job_time(t_start, t_end)
])
#print(df_res)
df_cache = pd.concat([
df_cache,
df_res[df_res.gram_ml != ''][['word', 'gram_ml', 'count']]
])
df_cache = df_cache.groupby(['word',
'gram_ml']).agg({'count': ['sum']})
df_cache.reset_index(inplace=True)
df_cache.index.name = 'idcorpus'
df_cache.columns = cache_columns
df_cache.sort_values(by=['count'], inplace=True, ascending=False)
#print(df_cache)
env.debug(1,
['POStagger', 'pos', 'Write ML cache to CSV:', file_cache])
df_cache.to_csv(file_cache, encoding='utf-8')
return df_res
def train(self,
df=pd.DataFrame(),
validation='eval',
n_splits=5,
b_smoketest=True,
n_frac=1):
env = Environment()
enc = Word_Encoder()
df_train = df
bgm_columns = env.bgm_columns_list(mode=1)
drop_columns = [
'word', 'gram', 's_suffix2', 's_suffix3', 's_prefix2', 's_prefix3',
'n_token'
] #, 'bgm_l_None'
#drop_columns.extend(['bgm_l_%s' % (i) for i in range(1, env.bgm_columns_max()) if 'bgm_l_%s' % (i) not in bgm_columns])
env.debug(1,
['POStagger', 'train',
'Drop colums: %s' % (drop_columns)])
if df_train.empty:
t_start = timer()
df_train = self.tokenz()
t_end = timer()
env.debug(1, [
'POSTagger', 'train', 'tokenz loaded:', 'time:',
env.job_time(t_start, t_end)
])
env.debug(1, [
'POStagger', 'train',
'All tokenz set shape %s' % df_train.shape[0]
])
t_start = timer()
env.debug(1, ['POStagger', 'train', 'Learning: START'])
if n_frac < 1:
df_train = df_train.sample(frac=n_frac)
env.debug(1, [
'POStagger', 'train',
'Training tokenz set shape %s' % df_train.shape[0]
])
#print(df_train.shape)
#df_train2 = df_train[bgm_columns]
#print(df_train2.shape)
#df_train2 = df_train2.astype({"idgram": int})
df_train = df_train.drop(columns=drop_columns, axis=1)
env.debug(
1, ['POStagger',
'Train colums: %s' % (df_train.columns.tolist())])
#print(df_train.columns)
#df_train = df_train.drop_duplicates() #slow-slow
#print(df_train.head())
df_train = df_train.fillna(0)
file_x = env.filename_xtrain_csv()
df_train.to_csv(file_x, encoding='utf-8')
env.debug(1, ['POStagger', 'train', 'Save X', file_x])
y = df_train['idgram'].values
df_train.drop(columns=['idgram'], inplace=True)
X = df_train.values
#array = df_train.values
#print(df_train)
#X = array[:, 1:]
#Y = array[:, 0]
#print(X, Y)
#validation_size = 0.20
seed = 241
frac_test_size = 0.2
sc = StandardScaler()
#Y_sc = sc.fit_transform(Y)
t2_start = timer()
if validation == 'cv': #Need cross-validation
scoring = 'accuracy'
# scoring = 'f1_samples'
kf = KFold(n_splits=n_splits, shuffle=True, random_state=seed)
if True: #Decision tree
env.debug(1, ['Tree cross-validation'])
# clf = DecisionTreeClassifier(criterion='gini', random_state=seed) # 0.79
# clf = KNeighborsClassifier(n_neighbors=230)
model = DecisionTreeClassifier(criterion='entropy',
random_state=seed) # 0.81
env.debug(
1, ['Calculate cross_val_score. Splits=%s' % (n_splits)])
scores = cross_val_score(model, X, y, cv=kf)
print('DTree scores:', scores.mean(), 'raw', scores)
if False: #Logistic regression
env.debug(1, ['LGR cross-validation'])
n_Cs = [0.01]
X = array[:, 5:]
X_sc = sc.fit_transform(X)
Y = df_train['idgram'].values
Y[Y > 0] = 1
print(X_sc, Y)
for n_c in n_Cs:
#clf = LogisticRegression(penalty='l2', solver='saga', C=n_c, multi_class='multinomial')
clf = LogisticRegression(penalty='l2',
solver='liblinear',
C=n_c)
# clf = SVC(kernel='linear', C=10000, random_state=241)
# clf = SVC(kernel='linear', C=0.01, random_state=seed)
# clf = SVC(random_state=seed)
# clf = Perceptron()
env.debug(1, [
'Calculate cross_val_score. Splits=%s C=%s' %
(n_splits, n_c)
])
scores = cross_val_score(clf, X_sc, Y, cv=kf)
print(scores)
if False: #GBM, RandomForest
env.debug(1, ['GBM cross-validation'])
asteps = [20] #GBM
#asteps=[100] #RandomForest
for i in asteps:
#clf = RandomForestClassifier(n_estimators=i)
clf = GradientBoostingClassifier(
n_estimators=i, max_depth=8) #, max_features='sqrt'
env.debug(1, [
'Calculate cross_val_score. Splits=%s Estimators=%s' %
(n_splits, i)
])
scores = cross_val_score(clf, X, Y, cv=kf)
print(scores)
if validation == 'eval':
# eval
model = xgb.XGBClassifier(n_estimators=140,
max_depth=16,
colsample=1,
subsample=0.5,
seed=seed)
X_train, X_test, y_train, y_test = train_test_split(
X,
y,
test_size=frac_test_size,
random_state=seed,
shuffle=True)
eval_set = [(X_train, y_train), (X_test, y_test)]
# print(eval_set)
f_eval = 'merror'
# f_eval = 'mlogloss'
model.fit(X_train,
y_train,
eval_metric=f_eval,
eval_set=eval_set,
verbose=False,
early_stopping_rounds=20)
ev_scores = model.evals_result()
ev_mean = np.array(ev_scores['validation_0'][f_eval]).mean()
#print(model.feature_importances_)
print(ev_mean, ev_scores)
xgb.plot_importance(model)
plt.show()
t2_end = timer()
t_end = timer()
env.debug(1, ['CV completed:', 'time:', env.job_time(t_start, t_end)])
if validation == 'cv':
#Training на всех данных
X_train, y_train = X, y
# model = SVC()
# model= DecisionTreeClassifier() #79
# model= LinearDiscriminantAnalysis() #47
# model=LogisticRegression() #48
# model = KNeighborsClassifier(n_neighbors=200) #48
# model = GaussianNB() #43
#print('Fit...')
#print('Validate...')
# predictions = model.predict(X_validation)
# print(accuracy_score(Y_validation, predictions))
# print(confusion_matrix(Y_validation, predictions))
# print(classification_report(Y_validation, predictions))
t_start = timer()
env.debug(1, ['Training: START'])
model.fit(X_train, y_train)
t_end = timer()
env.debug(1, ['Training: END', env.job_time(t_start, t_end)])
pickle.dump(sc, open(env.filename_scaler(), 'wb'))
pickle.dump(model, open(env.filename_model_tree(), 'wb'))
# Smoke test
if b_smoketest:
X_smoke_predict = [
'съеште', 'ещё', 'этих', 'мягких', 'французских', 'булок'
]
a_smoke = np.array(
[enc.word2token(elem) for elem in X_smoke_predict])
y_predictions = model.predict(a_smoke[:, 0:])
y_predictions_proba = model.predict(a_smoke[:, 0:])
#print(y_predictions)
print('Prediction', list(zip(X_smoke_predict, y_predictions)))
print('Proba', list(zip(X_smoke_predict, y_predictions_proba)))
return model