In [1]:
%load_ext autoreload
%autoreload 2
%matplotlib inline
In [2]:
import time
import xgboost as xgb
import lightgbm as lgb
# import category_encoders as cat_ed
import gc, mlcrate, glob
# from gplearn.genetic import SymbolicTransformer, SymbolicRegressor
from fastai.imports import *
from fastai.structured import *
from pandas_summary import DataFrameSummary
from sklearn.ensemble import RandomForestRegressor, ExtraTreesRegressor
from IPython.display import display
from catboost import CatBoostRegressor
from scipy.cluster import hierarchy as hc
from collections import Counter
from sklearn import metrics
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.metrics import roc_auc_score, log_loss
from sklearn.model_selection import KFold, StratifiedKFold
from sklearn.model_selection import GridSearchCV
from sklearn.decomposition import PCA, TruncatedSVD, FastICA, FactorAnalysis
from sklearn.random_projection import GaussianRandomProjection, SparseRandomProjection
from sklearn.cluster import KMeans
from sklearn.metrics import accuracy_score, log_loss
from sklearn.neighbors import KNeighborsRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import AdaBoostRegressor, GradientBoostingRegressor
from sklearn.naive_bayes import GaussianNB
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
from sklearn.neural_network import MLPRegressor
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import RBF
# will ignore all warning from sklearn, seaborn etc..
def ignore_warn(*args, **kwargs):
pass
warnings.warn = ignore_warn
pd.option_context("display.max_rows", 1000);
pd.option_context("display.max_columns", 1000);
In [3]:
PATH = os.getcwd()
PATH
Out[3]:
'D:\\Github\\fastai\\courses\\ml1\\AV_Enigma_ML'
In [5]:
df_raw = pd.read_csv(f'{PATH}\\train.csv', low_memory=False)
df_test = pd.read_csv(f'{PATH}\\test.csv', low_memory=False)
target = np.load(f'{PATH}\\target.npy')
In [6]:
df_raw.shape, df_test.shape, len(target)
Out[6]:
((330045, 7), (141448, 6), 330045)
In [7]:
man_train_list = df_raw.Username.unique()
man_test_list = df_test.Username.unique()
print("Train: {0}".format(len(man_train_list)))
print("Test: {0}".format(len(man_test_list)))
man_not_in_test = set(man_train_list) - set(man_test_list)
man_not_in_train = set(man_test_list) - set(man_train_list)
print("${}$ Users are featured in train but not in test".format(len(man_not_in_test)))
print("${}$ Users are featured in test but not in train".format(len(man_not_in_train)))
Train: 141802
Test: 79351
$96388$ Users are featured in train but not in test
$33937$ Users are featured in test but not in train
In [8]:
df_raw.shape, df_raw.drop(index = df_raw.loc[list(man_not_in_test)].index, inplace=True), df_raw.shape
Out[8]:
((330045, 7), None, (233657, 7))
In [9]:
print('Initial ', df_raw.shape, df_test.shape)
bin_cols = ['Answers', 'Views', 'Reputation']
for col in bin_cols:
ulimit = np.percentile(df_raw[col],99)
llimit = np.percentile(df_raw[col],1)
df_raw.drop(index=df_raw[col].ix[df_raw[col]>ulimit].index, inplace=True)
df_raw.drop(index=df_raw[col].ix[df_raw[col]<llimit].index, inplace=True)
print('Final ', df_raw.shape, df_test.shape)
Initial (233657, 7) (141448, 6)
Final (223134, 7) (141448, 6)
In [10]:
df_raw.drop(index= df_raw[(df_raw['Reputation'] == 0) & (df_raw['Answers'] !=0)].index, inplace=True)
In [11]:
df_raw.shape, df_test.shape
Out[11]:
((218673, 7), (141448, 6))
In [12]:
sns.distplot(df_raw['Answers'], kde=False)
Out[12]:
<matplotlib.axes._subplots.AxesSubplot at 0x14c24ea8828>
In [13]:
target = df_raw.Upvotes.values
In [14]:
np.mean(target)
Out[14]:
178.53712163824522
In [15]:
df_test.shape
Out[15]:
(141448, 6)
In [16]:
mean_preds = np.full_like(df_test['Username'], 178)
In [17]:
# ulimit = np.percentile(target,99)
# llimit = np.percentile(target,1)
# df_raw['Upvotes'].ix[df_raw['Upvotes']>ulimit] = ulimit
# df_raw['Upvotes'].ix[df_raw['Upvotes']<llimit] = llimit
sns.distplot(np.log(df_raw['Upvotes'] + 2), bins = 50, kde=False)
Out[17]:
<matplotlib.axes._subplots.AxesSubplot at 0x14c25f30438>
In [18]:
def display_all(df):
with pd.option_context("display.max_rows", 100):
with pd.option_context("display.max_columns", 100):
display(df)
def make_submission(probs):
sample = pd.read_csv(f'{PATH}\\sample_submission.csv')
submit = sample.copy()
submit['Upvotes'] = probs
return submit
In [19]:
xyz = pd.concat([df_raw.groupby('Username').mean(),df_raw.groupby('Username').count()], axis = 1).iloc[:,:-5]
xyz.columns = ['ID', 'Reputation', 'Answers', 'Views', 'Upvotes', 'count']
############################################################################################# Mean Aggs
df_raw_feats = pd.DataFrame()
df_raw_feats['Username'] = df_raw['Username'].copy()
df_raw_feats['Tag'] = df_raw['Tag'].copy()
df_raw_feats['Reputation'] = df_raw['Reputation'].copy()
df_raw_feats['Answers'] = df_raw['Answers'].copy()
unames = xyz.sort_values(by = 'count', ascending = False).reset_index()['Username'].values
count = xyz.sort_values(by = 'count', ascending = False).reset_index()['count'].values
answers = xyz.sort_values(by = 'count', ascending = False).reset_index()['Answers'].values
views = xyz.sort_values(by = 'count', ascending = False).reset_index()['Views'].values
repo = xyz.sort_values(by = 'count', ascending = False).reset_index()['Reputation'].values
d = {}
for idx,k in enumerate(unames):
d[k] = count[idx]
df_raw_feats['agg_count'] = df_raw['Username'].map(d)
d = {}
for idx,k in enumerate(unames):
d[k] = answers[idx]
df_raw_feats['agg_answers'] = df_raw['Username'].map(d)
d = {}
for idx,k in enumerate(unames):
d[k] = views[idx]
df_raw_feats['agg_views'] = df_raw['Username'].map(d)
d = {}
for idx,k in enumerate(unames):
d[k] = repo[idx]
df_raw_feats['agg_repo'] = df_raw['Username'].map(d)
In [20]:
xyz = pd.concat([df_test.groupby('Username').mean(),df_test.groupby('Username').count()], axis = 1).iloc[:,:-4]
xyz.columns = ['ID', 'Reputation', 'Answers', 'Views', 'count']
########################################################################################## Mean Aggregates
df_test_feats = pd.DataFrame()
df_test_feats['Username'] = df_test['Username'].copy()
df_test_feats['Tag'] = df_test['Tag'].copy()
df_test_feats['Reputation'] = df_test['Reputation'].copy()
df_test_feats['Answers'] = df_test['Answers']
unames = xyz.sort_values(by = 'count', ascending = False).reset_index()['Username'].values
count = xyz.sort_values(by = 'count', ascending = False).reset_index()['count'].values
answers = xyz.sort_values(by = 'count', ascending = False).reset_index()['Answers'].values
views = xyz.sort_values(by = 'count', ascending = False).reset_index()['Views'].values
repo = xyz.sort_values(by = 'count', ascending = False).reset_index()['Reputation'].values
d = {}
for idx,k in enumerate(unames):
d[k] = count[idx]
df_test_feats['agg_count'] = df_test['Username'].map(d)
d = {}
for idx,k in enumerate(unames):
d[k] = answers[idx]
df_test_feats['agg_answers'] = df_test['Username'].map(d)
d = {}
for idx,k in enumerate(unames):
d[k] = views[idx]
df_test_feats['agg_views'] = df_test['Username'].map(d)
d = {}
for idx,k in enumerate(unames):
d[k] = repo[idx]
df_test_feats['agg_repo'] = df_test['Username'].map(d)
In [21]:
df_raw_feats.head()
Out[21]:
Username
Tag
Reputation
Answers
agg_count
agg_answers
agg_views
agg_repo
4
112223
c
4271.0
4.0
3
2.00
20598.666667
4271.0
7
54623
j
2269.0
2.0
4
2.00
6063.750000
2269.0
8
172926
j
111.0
2.0
1
2.00
53738.000000
111.0
15
62155
c
7952.0
2.0
4
3.75
28314.750000
7952.0
16
43559
i
731.0
4.0
2
2.50
6730.500000
731.0
In [22]:
df_raw['Username'].min(), df_test['Username'].min()
Out[22]:
(3, 4)
In [23]:
add_trans = ['Reputation', 'Answers', 'Views']
for col in add_trans:
df_raw_feats[f'log_trans_{col}'.format(col)] = np.log(df_raw[col] + 1) #avoid log 0's if any
df_test_feats[f'log_trans_{col}'.format(col)] = np.log(df_test[col] + 1) #avoid log 0's if any
df_raw_feats[f'sqrt_trans_{col}'.format(col)] = np.sqrt(df_raw[col])
df_test_feats[f'sqrt_trans_{col}'.format(col)] = np.sqrt(df_test[col])
df_raw_feats['repo_per_Answers'] = df_raw['Reputation'] / (df_raw['Answers']+1)
df_raw_feats['repo_per_Views'] = df_raw['Reputation'] / df_raw['Views']
df_raw_feats['log_trans_repo_per_Answers'] = np.log(df_raw_feats['repo_per_Answers'] + 1)
df_raw_feats['log_trans_repo_per_Views'] = np.log(df_raw_feats['repo_per_Views'] + 1)
df_test_feats['repo_per_Answers'] = df_test['Reputation'] / (df_test['Answers'] +1)
df_test_feats['repo_per_Views'] = df_test['Reputation'] / df_test['Views']
df_test_feats['log_trans_repo_per_Answers'] = np.log(df_test_feats['repo_per_Answers'] + 1)
df_test_feats['log_trans_repo_per_Views'] = np.log(df_test_feats['repo_per_Views'] + 1)
df_raw.shape, df_test.shape, df_raw_feats.shape, df_test_feats.shape
Out[23]:
((218673, 7), (141448, 6), (218673, 18), (141448, 18))
In [24]:
unames_trn = df_raw[['Username','Tag']].groupby('Username')['Tag'].nunique().reset_index()['Username'].values
unames_test = df_test[['Username','Tag']].groupby('Username')['Tag'].nunique().reset_index()['Username'].values
tag_trn = df_raw[['Username','Tag']].groupby('Username')['Tag'].nunique().reset_index()['Tag'].values
tag_test = df_test[['Username','Tag']].groupby('Username')['Tag'].nunique().reset_index()['Tag'].values
In [25]:
d = {}
for idx,k in enumerate(unames_trn):
d[k] = tag_trn[idx]
df_raw_feats['unique_tag'] = df_raw['Username'].map(d)
In [26]:
d = {}
for idx,k in enumerate(unames_test):
d[k] = tag_test[idx]
df_test_feats['unique_tag'] = df_test['Username'].map(d)
In [27]:
def get_score(l = []):
score = 1
for i in l:
if i == 'c': score += 10
if i == 'j': score += 9
if i == 'p': score += 8
if i == 'i': score += 7
if i == 'a': score += 6
if i == 's': score += 5
if i == 'h': score += 4
if i == 'o': score += 3
if i == 'r': score += 2
return(score)
In [28]:
%%time
import gc
d = {}
for i in unames_trn[::-1]:
d[i] = set(df_raw_feats[df_raw_feats['Username'] == i]['Tag'].values)
for k,v in d.items():
l = []
for i in v:
l.append(i)
d[k] = get_score(l)
df_raw_feats['skill_score'] = df_raw_feats['Username'].map(d)
del d
gc.collect()
Wall time: 1min 25s
In [29]:
%%time
d = {}
for i in unames_test[::-1]:
d[i] = set(df_test_feats[df_test_feats['Username'] == i]['Tag'].values)
for k,v in d.items():
l = []
for i in v:
l.append(i)
d[k] = get_score(l)
df_test_feats['skill_score'] = df_test_feats['Username'].map(d)
del d
gc.collect()
Wall time: 57.2 s
In [30]:
df_raw_feats['rep_per_skill'] = df_raw['Reputation']/ df_raw_feats['skill_score']
df_raw_feats['skill_per_tag'] = df_raw_feats['skill_score']/ df_raw_feats['unique_tag']
df_raw_feats['views_per_ans'] = df_raw['Views'] / (df_raw['Answers']+ 1)
df_test_feats['rep_per_skill'] = df_test['Reputation']/ df_test_feats['skill_score']
df_test_feats['skill_per_tag'] = df_test_feats['skill_score']/ df_test_feats['unique_tag']
df_test_feats['views_per_ans'] = df_test['Views'] / (df_test['Answers']+ 1)
In [31]:
plt.scatter(range(df_raw.shape[0]), np.sort(np.log(target+2)))
Out[31]:
<matplotlib.collections.PathCollection at 0x14c28d18e48>
In [31]:
df_raw_feats.to_csv(f'{PATH}\\new_feats_train.csv', index=None)
df_test_feats.to_csv(f'{PATH}\\new_feats_test.csv', index=None)
In [56]:
bins = [0, 1, 5, 10, 25, 50, 75, 100]
labels = [i+1 for i in range(len(bins) - 1)]
bin_cols = ['Answers', 'Views', 'Reputation']
for col in bin_cols:
df_raw_feats[f'bin_{col}'.format(col)] = pd.cut(np.log(df_raw[col]+2) ,bins,labels = labels)
df_test_feats[f'bin_{col}'.format(col)] = pd.cut(np.log(df_test[col]+2),bins,labels = labels)
In [32]:
df_raw_feats['Tag'] = df_raw_feats['Tag'].astype('category')
df_test_feats['Tag'] = df_test_feats['Tag'].astype('category')
#couldhave ued hstack as well...
In [57]:
df_raw_feats.get_ftype_counts(), df_test_feats.get_ftype_counts()
Out[57]:
(category:dense 4
float64:dense 18
int64:dense 4
dtype: int64, category:dense 4
float64:dense 18
int64:dense 4
dtype: int64)
In [35]:
target
Out[35]:
array([ 83., 3., 79., ..., 15., 3., 37.])
In [43]:
model=CatBoostRegressor(iterations=300, learning_rate= 0.06, depth = 6, loss_function='RMSE', )
In [42]:
df_raw_feats.shape, df_test_feats.shape, len(target)
Out[42]:
((218673, 23), (141448, 23), 218673)
In [45]:
model.fit(df_raw_feats, target,cat_features=[1,23,24,25], eval_set=(X_valid,y_valid))
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bestTest = 213.9201608
bestIteration = 299
Out[45]:
<catboost.core.CatBoostRegressor at 0x2598a706438>
In [265]:
model.save_model(f'{PATH}\\catboost_new_feats_model', export_parameters=dict())
In [46]:
preds = model.predict(df_test_feats);
preds[:10]
Out[46]:
array([ 185.86597, 104.54369, 28.44292, 20.38781, 303.19668, 17.94036, 16.89321, 88.93819,
52.39601, 16.89539])
In [59]:
np.mean(target), np.std(target), np.max(target)
Out[59]:
(178.53712163824522, 715.88228024998364, 36374.0)
In [60]:
np.mean(preds), np.std(preds), np.max(preds)
Out[60]:
(237.93752569435802, 1041.744052814126, 27649.059268086836)
In [64]:
clipped_preds = np.where(preds>15000,15000,preds )
In [65]:
submit = make_submission(clipped_preds)
In [66]:
submit.to_csv(f'{PATH}\\Adi_catboost_with_new_feats_10092018.csv', index=None)
In [34]:
from sklearn.model_selection import train_test_split
X_train, X_valid, y_train, y_valid = train_test_split(pd.get_dummies(df_raw_feats,prefix='dummy'), target, test_size=0.25, random_state=42)
In [46]:
from sklearn.metrics import mean_squared_error as mse
def runXGB(train_X, train_y, test_X, test_y=None):
params = {}
params['booster'] = 'gblinear'
params["objective"] = "gpu:reg:linear"
params["eta"] = 0.02
params["min_child_weight"] = 2
params["subsample"] = 0.9
params["colsample_bytree"] = 0.8
params["silent"] = 0
params["max_depth"] = 8
params["seed"] = 1
params['alpha'] = .05
#params['tree_method'] = 'gpu_hist'
params['gamma'] = 3
plst = list(params.items())
num_rounds = 900
xgtrain = xgb.DMatrix(train_X, label=train_y)
xgtest = xgb.DMatrix(test_X)
model = xgb.train(plst, xgtrain, num_rounds)
pred_test_y = model.predict(xgtest)
return model, pred_test_y
def rmse(act_y, pred_y):
return np.sqrt(mse(act_y, pred_y))
In [47]:
model_xgb, preds = runXGB(pd.get_dummies(df_raw_feats,prefix='dummy'), target, pd.get_dummies(df_test_feats,prefix='dummy'))
In [48]:
preds
Out[48]:
array([ 372.51456, 614.90576, 214.76334, ..., 177.34044, 5.16377, 58.18262], dtype=float32)
In [49]:
submit = make_submission(preds)
submit.to_csv(f'{PATH}\\xgb_gblinear_preds.csv', index=None)
In [290]:
import joblib
In [291]:
joblib.dump(my_xgb, f'{PATH}\\xgb_model')
Out[291]:
['D:\\Github\\fastai\\courses\\ml1\\AV_Enigma_ML\\xgb_model']
In [292]:
xgb_model_load = joblib.load(f'{PATH}\\xgb_model')
In [293]:
xgb_model_load.feature_importances_
Out[293]:
array([ 0.02511, 0.01012, 0.01531, 0.01939, 0.039 , 0.34472, 0.02941, 0.0075 , 0.00027, 0.00417,
0.00018, 0.37205, 0.02329, 0.0134 , 0.02684, 0.00262, 0.00364, 0.00155, 0.00421, 0.01442,
0.00705, 0.01721, 0.00342, 0.00013, 0.00266, 0.00031, 0.00164, 0.00031, 0.00262, 0.00324,
0.00164, 0.00075, 0.00067, 0.00115], dtype=float32)
In [135]:
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras import metrics
In [127]:
df_raw_feats_dummy = pd.get_dummies(df_raw_feats.drop(['bin_Reputation','bin_Views','bin_Answers'],axis=1),prefix='dummy')
df_test_feats_dummy = pd.get_dummies(df_test_feats.drop(['bin_Reputation','bin_Views','bin_Answers'],axis=1),prefix='dummy')
In [142]:
df_raw_feats_dummy.shape, df_test_feats_dummy.shape
Out[142]:
((218673, 32), (141448, 32))
In [155]:
scaler = StandardScaler()
scaler.fit(df_raw_feats_dummy)
Out[155]:
StandardScaler(copy=True, with_mean=True, with_std=True)
In [161]:
X_train = scaler.fit_transform(df_raw_feats_dummy)
X_test = scaler.transform(df_test_feats_dummy)
In [168]:
model = Sequential()
model.add(Dense(100, input_dim=df_raw_feats_dummy.shape[1], activation='relu'))
model.add(Dense(50, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(16, activation='relu'))
model.add(Dense(1))
In [169]:
model.summary()
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_13 (Dense) (None, 100) 3300
_________________________________________________________________
dense_14 (Dense) (None, 50) 5050
_________________________________________________________________
dropout_4 (Dropout) (None, 50) 0
_________________________________________________________________
dense_15 (Dense) (None, 16) 816
_________________________________________________________________
dense_16 (Dense) (None, 1) 17
=================================================================
Total params: 9,183
Trainable params: 9,183
Non-trainable params: 0
_________________________________________________________________
In [170]:
model.compile(loss='mean_squared_error', optimizer='sgd',metrics=['mae'])
In [171]:
model.fit(X_train, np.log(target+2), 64, validation_split=0.2)
Train on 174938 samples, validate on 43735 samples
Epoch 1/10
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Epoch 4/10
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Epoch 5/10
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Epoch 6/10
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Epoch 8/10
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Epoch 10/10
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Out[171]:
<keras.callbacks.History at 0x2599ead5cc0>
In [177]:
preds = np.exp(model.predict(X_test).flatten()) -2
In [178]:
preds
Out[178]:
array([ 130.00826, 59.6307 , 28.58158, ..., 38.07769, 11.17347, 20.13352], dtype=float32)
In [179]:
submit = make_submission(preds)
In [180]:
max(preds)
Out[180]:
365183.53
In [182]:
submit.to_csv(f'{PATH}\\nn1.csv', index=None)
In [58]:
df_raw_feats.head(3)
Out[58]:
Username
Tag
Reputation
Answers
agg_count
agg_answers
agg_views
agg_repo
log_trans_Reputation
sqrt_trans_Reputation
...
log_trans_repo_per_Answers
log_trans_repo_per_Views
unique_tag
skill_score
rep_per_skill
skill_per_tag
views_per_ans
bin_Answers
bin_Views
bin_Reputation
4
112223
c
4271.0
4.0
3
2.0
20598.666667
4271.0
8.359837
65.352888
...
6.751335
0.266492
1
11
388.272727
11.0
2797.200000
2
3
3
7
54623
j
2269.0
2.0
4
2.0
6063.750000
2269.0
7.727535
47.634021
...
6.629803
2.112929
2
20
113.450000
10.0
104.000000
2
3
3
8
172926
j
111.0
2.0
1
2.0
53738.000000
111.0
4.718499
10.535654
...
3.637586
0.002063
1
10
11.100000
10.0
17912.666667
2
4
2
3 rows × 26 columns
In [59]:
from sklearn.feature_extraction.text import TfidfVectorizer, TfidfTransformer
In [60]:
idx_split = df_raw_feats.shape[0]
In [61]:
full_df = pd.concat([df_raw_feats, df_test_feats])
In [77]:
cats = ['Tag', 'bin_Answers', 'bin_Views', 'bin_Reputation', 'Username']
In [86]:
full_cats = full_df[cats].astype('str')
lst = full_cats[cats].as_matrix().tolist()
flat_list = [' '.join(sublist) for sublist in lst]
vect = TfidfVectorizer(ngram_range=(1,3), max_features=1000,analyzer='word')
tfidf_matrix = vect.fit_transform(flat_list)
X_train_tf = tfidf_matrix[:idx_split]
X_test_tf = tfidf_matrix[idx_split:]
X_train_tf.shape, X_test_tf.shape
Out[86]:
((218673, 1000), (141448, 1000))
In [87]:
from scipy.sparse import csr_matrix
from scipy.sparse import hstack
In [88]:
x_train_full = csr_matrix(hstack([X_train_tf,df_raw_feats.drop(cats,axis=1).values]))
x_test_full = csr_matrix(hstack([X_test_tf ,df_test_feats.drop(cats,axis=1).values]))
x_train_full.shape,x_test_full.shape
Out[88]:
((218673, 1021), (141448, 1021))
In [89]:
from sklearn.metrics import mean_squared_error as mse
def runXGB(train_X, train_y, test_X, test_y=None):
params = {}
params['booster'] = 'gbtree'
params["objective"] = "gpu:reg:linear"
params["eta"] = 0.02
params["min_child_weight"] = 2
params["subsample"] = 0.9
params["colsample_bytree"] = 0.8
params["silent"] = 1
params["max_depth"] = 8
params["seed"] = 1
params['alpha'] = .05
params['tree_method'] = 'gpu_hist'
params['gamma'] = 3
plst = list(params.items())
num_rounds = 900
xgtrain = xgb.DMatrix(train_X, label=train_y)
xgtest = xgb.DMatrix(test_X)
model = xgb.train(plst, xgtrain, num_rounds)
pred_test_y = model.predict(xgtest)
return model, pred_test_y
def rmse(act_y, pred_y):
return np.sqrt(mse(act_y, pred_y))
In [90]:
model_xgb, preds = runXGB(x_train_full, target, x_test_full)
In [91]:
preds
Out[91]:
array([ 231.71132, 84.4279 , 54.15084, ..., 65.54631, 18.16169, 27.44318], dtype=float32)
In [92]:
submit = make_submission(preds)
submit.to_csv(f'{PATH}\\xgb_with_tfidf.csv', index=False)
In [ ]:
Content source: AdityaSoni19031997/Machine-Learning
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