Kaggle competition: Natural Language Processing with Disaster Tweets
pv = nett.predict(xv, batch_size=50_000, verbose=1)
 pe = nett.predict(xe, batch_size=50_000, verbose=1 )<compute_test_metric>
%matplotlib inline
PUNCT_TO_REMOVE = string.punctuation
STOPWORDS = set(stopwords.words('english'))
stemmer = PorterStemmer()
nett.evaluate(xv, yv, batch_size=50_000 )<define_variables>
df_train = pd.read_csv('.. /input/nlp-getting-started/train.csv')
 df_test = pd.read_csv('.. /input/nlp-getting-started/test.csv' )
names = [f"F{i+1}" for i in range(28)]<create_dataframe>
def text_preprocessing(text):

 text = text.lower()
text = re.compile(r'https?://\S+|www\.\S+' ).sub(r'', text)
 text = text.translate(str.maketrans('', '', PUNCT_TO_REMOVE))
text = " ".join([word for word in str(text ).split() if word not in STOPWORDS])
 text = " ".join([stemmer.stem(word)for word in text.split() ])
 return text
piv = pd.DataFrame(ids[:, 0], columns=["id"] )<feature_engineering>
df_train['text_processed'] = df_train['text'].apply(text_preprocessing)
 df_test['text_processed'] = df_test['text'].apply(text_preprocessing )
QUANTILES = ["0.005", "0.025", "0.165", "0.250", "0.500", "0.750", "0.835", "0.975", "0.995"]
VALID = []
EVAL = []
for i, quantile in tqdm(enumerate(QUANTILES)) :
t1 = pd.DataFrame(pv[:,:, i]*sv, columns=names)
 t1 = piv.join(t1)
 t1["id"] = t1["id"] + f"_{quantile}_validation"
t2 = pd.DataFrame(pe[:,:, i]*se, columns=names)
 t2 = piv.join(t2)
 t2["id"] = t2["id"] + f"_{quantile}_evaluation"
VALID.append(t1)
 EVAL.append(t2)
<create_dataframe>
X = df_train['text_processed']
y = df_train['target']
sub = pd.DataFrame()
sub = sub.append(VALID + EVAL)
 del VALID, EVAL, t1, t2<save_to_csv>
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.1,random_state=42 )
sub.to_csv("submission.csv", index=False )<import_modules>
vectorizer=TfidfVectorizer(ngram_range=(1,3),min_df=3,strip_accents='unicode',
use_idf=1,smooth_idf=1, sublinear_tf=1,max_features=None)
 vectorizer.fit(list(df_train['text_processed'])+list(df_test['text_processed']))
print('vocab length',len(vectorizer.vocabulary_))
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import lightgbm as lgb
from sklearn.metrics import mean_squared_error
from tqdm import tqdm
import gc<categorify>
X_train_onehot = vectorizer.transform(X_train ).todense()
X_val_onehot = vectorizer.transform(X_val ).todense()
def preprocess_sales(sales, start=1400, upper=1970):
if start is not None:
print("dropping...")
 to_drop = [f"d_{i+1}" for i in range(start-1)]
print(sales.shape)
 sales.drop(to_drop, axis=1, inplace=True)
 print(sales.shape)
 print("adding...")
 new_columns = ['d_%i'%i for i in range(1942, upper, 1)]
for col in new_columns:
sales[col] = np.nan
print("melting...")
 sales = sales.melt(id_vars=["id", "item_id", "dept_id", "cat_id", "store_id", "state_id","scale","start"],
var_name='d', value_name='demand')
 print("generating order")
 if start is not None:
skip = start
else:
skip = 1
sales["nb"] =sales.index // 42840 + skip
return sales
def preprocess_calendar(calendar):
global maps, mods
calendar["event_name"] = calendar["event_name_1"]
calendar["event_type"] = calendar["event_type_1"]
map1 = {mod:i for i,mod in enumerate(calendar['event_name'].unique())}
calendar['event_name'] = calendar['event_name'].map(map1)
 map2 = {mod:i for i,mod in enumerate(calendar['event_type'].unique())}
calendar['event_type'] = calendar['event_type'].map(map2)
 calendar['nday'] = calendar['date'].str[-2:].astype(int)
 maps["event_name"] = map1
maps["event_type"] = map2
mods["event_name"] = len(map1)
 mods["event_type"] = len(map2)
 calendar["wday"] -=1
calendar["month"] -=1
calendar["year"] -= 2011
mods["month"] = 12
mods["year"] = 6
mods["wday"] = 7
mods['snap_CA'] = 2
mods['snap_TX'] = 2
mods['snap_WI'] = 2
calendar.drop(["event_name_1", "event_name_2", "event_type_1", "event_type_2", "date", "weekday"],
axis=1, inplace=True)
 return calendar
def make_dataset(categorize=False ,start=1400, upper= 1970):
global maps, mods
print("loading calendar...")
 calendar = pd.read_csv(".. /input/m5-forecasting-uncertainty/calendar.csv")
 print("loading sales...")
 sales = pd.read_csv(".. /input/walmartadd/sales.csv")
 cols = ["item_id", "dept_id", "cat_id","store_id","state_id"]
if categorize:
for col in cols:
temp_dct = {mod:i for i, mod in enumerate(sales[col].unique())}
mods[col] = len(temp_dct)
 maps[col] = temp_dct
for col in cols:
sales[col] = sales[col].map(maps[col])
 sales =preprocess_sales(sales, start=start, upper= upper)
 calendar = preprocess_calendar(calendar)
 calendar = reduce_mem_usage(calendar)
 print("merge with calendar...")
 sales = sales.merge(calendar, on='d', how='left')
 del calendar
print("reordering...")
 sales.sort_values(by=["id","nb"], inplace=True)
 print("re-indexing.. ")
 sales.reset_index(inplace=True, drop=True)
 gc.collect()
sales['n_week'] =(sales['nb']-1)//7
sales["nday"] -= 1
mods['nday'] = 31
sales = reduce_mem_usage(sales)
 gc.collect()
return sales 
<create_dataframe>
lr_clf = LogisticRegression(max_iter=150,penalty='l2',solver='lbfgs',random_state=0)
 lr_clf.fit(X_train_onehot, y_train)
 lr_pred = lr_clf.predict(X_val_onehot)
 print('accuracy score: ',accuracy_score(lr_pred,y_val))
print(classification_report(y_val, lr_pred))
%%time
CATEGORIZE = True;
START = 1000; UPPER = 1970;
maps = {}
mods = {}
sales = make_dataset(categorize=CATEGORIZE ,start=START, upper= UPPER )<feature_engineering>
logloss_lr = log_loss(y_val,lr_clf.predict_proba(X_val_onehot))
print('logloss_lr:',logloss_lr )
sales["x"] = sales["demand"] / sales["scale"]<define_variables>
mnb_clf = MultinomialNB()
mnb_clf.fit(X_train_onehot, y_train)
 mnb_pred = mnb_clf.predict(X_val_onehot)
 print('accuracy score: ',accuracy_score(mnb_pred,y_val))
print(classification_report(y_val, mnb_pred))
nb = sales['nb'].values
MAX_LAG = max(LAGS)
 tr_mask = np.logical_and(nb>START + MAX_LAG, nb<=1941)
 val_mask = np.logical_and(nb>1913, nb<=1941)
 te_mask = np.logical_and(nb>1941, nb<=1969 )<prepare_x_and_y>
logloss_mnb = log_loss(y_val,mnb_clf.predict_proba(X_val_onehot))
print('logloss_mnb:',logloss_mnb )
scale = sales['scale'].values
ids = sales['id'].values
ys = sales['x'].values
Z = sales[FEATS].values<train_model>
rf_clf = RandomForestClassifier(random_state=0,n_estimators=100,
max_depth=None, verbose=0,n_jobs=-1)
 rf_clf.fit(X_train_onehot, y_train)
 rf_pred = rf_clf.predict(X_val_onehot)
 print('accuracy score: ',accuracy_score(rf_pred,y_val))
print(classification_report(y_val, rf_pred))
scale<normalization>
logloss_rf = log_loss(y_val,rf_clf.predict_proba(X_val_onehot))
print('logloss_rf:',logloss_rf )
sv = scale[val_mask]
se = scale[te_mask]
ids = ids[te_mask]
ids = ids.reshape(( -1, 28))<prepare_x_and_y>
xgb_clf = xgb.XGBClassifier(n_estimators=100,n_jobs=-1,max_depth=15,
min_child_weight=3,objective='binary:logistic'
,colsample_bytree=0.4)
 xgb_clf.fit(X_train_onehot, y_train)
 xgb_predictions = xgb_clf.predict(X_val_onehot)
 print('accuracy score: ',accuracy_score(xgb_predictions,y_val))
print(classification_report(y_val, xgb_predictions))
ca = sales[['snap_CA']].values
tx = sales[['snap_TX']].values
wi = sales[['snap_WI']].values
wday = sales[['wday']].values
month = sales[['month']].values
year = sales[['year']].values
event = sales[['event_name']].values
nday = sales[['nday']].values
lag1=sales[['x_28']].values<define_variables>
logloss_xgb = log_loss(y_val,xgb_clf.predict_proba(X_val_onehot))
print('logloss_rf:',logloss_xgb )
item = sales[['item_id']].values
dept = sales[['dept_id']].values
cat = sales[['cat_id']].values
store = sales[['store_id']].values
state = sales[['state_id']].values<drop_column>
model = Sequential()
model.add(Dense(512, activation='relu', input_dim=np.shape(X_train_onehot)[1],
kernel_regularizer=regularizers.l2(0.01),
activity_regularizer=regularizers.l1(0.01)))
 model.add(BatchNormalization())
 model.add(Dropout(0.5))
model.add(Dense(256, activation='relu'))
model.add(BatchNormalization())
 model.add(Dropout(0.6))
model.add(Dense(64, activation='relu'))
model.add(BatchNormalization())
 model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
adam = Adam(lr=1e-4, beta_1=0.9, beta_2=0.999, epsilon=10**-8, decay=0.0001, amsgrad=False)
 model.compile(optimizer= adam,
loss='binary_crossentropy',
metrics=['accuracy'])
 print(model.summary())
 hist = model.fit(X_train_onehot, y_train,validation_data =(X_val_onehot,y_val), epochs=20, batch_size=16 )
def make_data(mask):
x = {"snap_CA":ca[mask], "snap_TX":tx[mask], "snap_WI":wi[mask], "wday":wday[mask],
"month":month[mask], "year":year[mask], "event":event[mask], "nday":nday[mask],
"item":item[mask], "dept":dept[mask], "cat":cat[mask], "store":store[mask],
"state":state[mask],"num":Z[mask]}
t = ys[mask]
return x, t<prepare_x_and_y>
dnn_pred = model.predict_classes(X_val_onehot )
xt, yt = make_data(tr_mask)
 xv, yv = make_data(val_mask)
 xe, ye = make_data(te_mask )<import_modules>
print('accuracy score: ',accuracy_score(dnn_pred,y_val))
print(classification_report(y_val, dnn_pred))
import tensorflow.keras.layers as L
import tensorflow.keras.models as M
import tensorflow.keras.backend as K
from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
import tensorflow as tf<define_search_space>
logloss_dnn = log_loss(y_val,model.predict_proba(X_val_onehot))
print('logloss_dnn:',logloss_dnn )
qs = [0.005, 0.025, 0.165, 0.250, 0.500, 0.750, 0.835, 0.975, 0.995]<concatenate>
lr_predictions_val = lr_clf.predict_proba(X_val_onehot)
 mnb_predictions_val = mnb_clf.predict_proba(X_val_onehot)
 xgb_predictions_val = xgb_clf.predict_proba(X_val_onehot)
q = tf.constant(np.array([qs]), dtype=tf.float32)
 q<feature_engineering>
predictions_val = 1/3*lr_predictions_val[:,1]+1/3*mnb_predictions_val[:,1] \
+1/3*xgb_predictions_val[:,1]
predictions_val = np.where(predictions_val>0.5, 1, 0 )
ca = L.Input(( 1,), name="snap_CA")
 ca<compute_test_metric>
print('accuracy score: ',accuracy_score(predictions_val,y_val))
print(classification_report(y_val, predictions_val))
def qloss(y_true, y_pred):
qs = [0.005, 0.025, 0.165, 0.250, 0.500, 0.750, 0.835, 0.975, 0.995]
q = tf.constant(np.array([qs]), dtype=tf.float32)
 e = y_true - y_pred
v = tf.maximum(q*e,(q-1)*e)
 return K.mean(v)
 def make_model(n_in):
num = L.Input(( n_in,), name="num")
 ca = L.Input(( 1,), name="snap_CA")
 tx = L.Input(( 1,), name="snap_TX")
 wi = L.Input(( 1,), name="snap_WI")
 wday = L.Input(( 1,), name="wday")
 month = L.Input(( 1,), name="month")
 year = L.Input(( 1,), name="year")
 event = L.Input(( 1,), name="event")
 nday = L.Input(( 1,), name="nday")
 item = L.Input(( 1,), name="item")
 dept = L.Input(( 1,), name="dept")
 cat = L.Input(( 1,), name="cat")
 store = L.Input(( 1,), name="store")
 state = L.Input(( 1,), name="state")
 inp = {"snap_CA":ca, "snap_TX":tx, "snap_WI":wi, "wday":wday,
"month":month, "year":year, "event":event, "nday":nday,
"item":item, "dept":dept, "cat":cat, "store":store,
"state":state, "num":num}
ca_ = L.Embedding(mods["snap_CA"], mods["snap_CA"], name="ca_3d" )(ca)
 tx_ = L.Embedding(mods["snap_TX"], mods["snap_TX"], name="tx_3d" )(tx)
 wi_ = L.Embedding(mods["snap_WI"], mods["snap_WI"], name="wi_3d" )(wi)
 wday_ = L.Embedding(mods["wday"], mods["wday"], name="wday_3d" )(wday)
 month_ = L.Embedding(mods["month"], mods["month"], name="month_3d" )(month)
 year_ = L.Embedding(mods["year"], mods["year"], name="year_3d" )(year)
 event_ = L.Embedding(mods["event_name"], mods["event_name"], name="event_3d" )(event)
 nday_ = L.Embedding(mods["nday"], mods["nday"], name="nday_3d" )(nday)
 item_ = L.Embedding(mods["item_id"], 10, name="item_3d" )(item)
 dept_ = L.Embedding(mods["dept_id"], mods["dept_id"], name="dept_3d" )(dept)
 cat_ = L.Embedding(mods["cat_id"], mods["cat_id"], name="cat_3d" )(cat)
 store_ = L.Embedding(mods["store_id"], mods["store_id"], name="store_3d" )(store)
 state_ = L.Embedding(mods["state_id"], mods["state_id"], name="state_3d" )(state)
 p = [ca_, tx_, wi_, wday_, month_, year_, event_, nday_, item_, dept_, cat_, store_, state_]
emb = L.Concatenate(name="embds" )(p)
 context = L.Flatten(name="context" )(emb)
 x = L.Concatenate(name="x1" )([context, num])
 x = L.Dense(500, activation="relu", name="d1" )(x)
 x = L.Dropout(0.3 )(x)
 x = L.Concatenate(name="m1" )([x, context])
 x = L.Dense(500, activation="relu", name="d2" )(x)
 x = L.Dropout(0.3 )(x)
 x = L.Concatenate(name="m2" )([x, context])
 x = L.Dense(500, activation="relu", name="d3" )(x)
 preds = L.Dense(9, activation="linear", name="preds" )(x)
 model = M.Model(inp, preds, name="M1")
 model.compile(loss=qloss, optimizer="adam")
 return model 
<choose_model_class>
df_test = pd.read_csv('.. /input/nlp-getting-started/test.csv' )
net = make_model(len(FEATS))
ckpt = ModelCheckpoint("w.h5", monitor='val_loss', verbose=1, save_best_only=True,mode='min')
 reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2,
patience=5, min_lr=0.001)
 es = EarlyStopping(monitor='val_loss', patience=3)
 print(net.summary() )<train_model>
df_test['text_processed'] = df_test['text'].apply(text_preprocessing )
net.fit(xt, yt, batch_size=50_000, epochs=20, validation_data=(xv, yv), callbacks=[ckpt, reduce_lr, es] )<load_pretrained>
X_test = df_test['text_processed']
X_test_onehot = vectorizer.transform(X_test ).todense()
nett = make_model(len(FEATS))
nett.load_weights("w.h5" )<predict_on_test>
lr_predictions = lr_clf.predict_proba(X_test_onehot)
 mnb_predictions = mnb_clf.predict_proba(X_test_onehot)
 xgb_predictions = xgb_clf.predict_proba(X_test_onehot)
pv = nett.predict(xv, batch_size=50_000, verbose=1)
 pe = nett.predict(xe, batch_size=50_000, verbose=1 )<compute_test_metric>
predictions = 1/3*lr_predictions[:,1]+1/3*mnb_predictions[:,1]+1/3*xgb_predictions[:,1]
nett.evaluate(xv, yv, batch_size=50_000 )<define_variables>
predictions = np.where(predictions>0.5, 1, 0 )
names = [f"F{i+1}" for i in range(28)]<create_dataframe>
df_submission = pd.read_csv('.. /input/nlp-getting-started/sample_submission.csv')
 df_submission['target'] = predictions
df_submission.to_csv('submission.csv',index=False )