AV_Enigma_ML_retrying


boring stuff


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

fe's


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)

cat


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)

xgb


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)

NN


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
 42304/174938 [======>.......................] - ETA: 1077s - loss: 16.7450 - mean_absolute_error: 3.631 - ETA: 170s - loss: 9.5557 - mean_absolute_error: 2.6167  - ETA: 96s - loss: 6.3488 - mean_absolute_error: 1.918 - ETA: 73s - loss: 4.8702 - mean_absolute_error: 1.64 - ETA: 62s - loss: 3.9035 - mean_absolute_error: 1.43 - ETA: 55s - loss: 3.3155 - mean_absolute_error: 1.30 - ETA: 50s - loss: 2.8769 - mean_absolute_error: 1.20 - ETA: 48s - loss: 2.6435 - mean_absolute_error: 1.14 - ETA: 44s - loss: 2.4108 - mean_absolute_error: 1.09 - ETA: 41s - loss: 2.1664 - mean_absolute_error: 1.02 - ETA: 40s - loss: 2.0341 - mean_absolute_error: 0.99 - ETA: 40s - loss: 1.9350 - mean_absolute_error: 0.97 - ETA: 39s - loss: 1.8478 - mean_absolute_error: 0.94 - ETA: 38s - loss: 1.7663 - mean_absolute_error: 0.92 - ETA: 37s - loss: 1.7033 - mean_absolute_error: 0.91 - ETA: 37s - loss: 1.6338 - mean_absolute_error: 0.89 - ETA: 36s - loss: 1.5816 - mean_absolute_error: 0.88 - ETA: 36s - loss: 1.5368 - 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Epoch 2/10
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Epoch 4/10
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Epoch 5/10
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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)

ranodm exp


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 [ ]: