Kaggle competition: PetFinder.my Adoption Prediction
<SOS> metric: QuadraticWeightedKappa Kaggle data source: petfinder.my-adoption-prediction<merge>
np.random.seed(369 )
df_train = df_train.merge(df_neigh, on='id' )<define_variables>
def confusion_matrix(rater_a, rater_b, min_rating=None, max_rating=None):

 assert(len(rater_a)== len(rater_b))
if min_rating is None:
min_rating = min(rater_a + rater_b)
 if max_rating is None:
max_rating = max(rater_a + rater_b)
 num_ratings = int(max_rating - min_rating + 1)
 conf_mat = [[0 for i in range(num_ratings)]
for j in range(num_ratings)]
for a, b in zip(rater_a, rater_b):
conf_mat[a - min_rating][b - min_rating] += 1
return conf_mat
def histogram(ratings, min_rating=None, max_rating=None):

 if min_rating is None:
min_rating = min(ratings)
 if max_rating is None:
max_rating = max(ratings)
 num_ratings = int(max_rating - min_rating + 1)
 hist_ratings = [0 for x in range(num_ratings)]
for r in ratings:
hist_ratings[r - min_rating] += 1
return hist_ratings
def quadratic_weighted_kappa(y, y_pred):

 rater_a = y
rater_b = y_pred
min_rating=None
max_rating=None
rater_a = np.array(rater_a, dtype=int)
 rater_b = np.array(rater_b, dtype=int)
 assert(len(rater_a)== len(rater_b))
if min_rating is None:
min_rating = min(min(rater_a), min(rater_b))
if max_rating is None:
max_rating = max(max(rater_a), max(rater_b))
conf_mat = confusion_matrix(rater_a, rater_b,
min_rating, max_rating)
 num_ratings = len(conf_mat)
 num_scored_items = float(len(rater_a))
hist_rater_a = histogram(rater_a, min_rating, max_rating)
 hist_rater_b = histogram(rater_b, min_rating, max_rating)
 numerator = 0.0
denominator = 0.0
for i in range(num_ratings):
for j in range(num_ratings):
expected_count =(hist_rater_a[i] * hist_rater_b[j]
/ num_scored_items)
 d = pow(i - j, 2.0)/ pow(num_ratings - 1, 2.0)
 numerator += d * conf_mat[i][j] / num_scored_items
denominator += d * expected_count / num_scored_items
return(1.0 - numerator / denominator )
n_neighbors = 5
neigh_dm_cols_2 = [f'dm_med2_neighbor{i}' for i in range(1, n_neighbors+1)]
neigh_d0_cols_2 = [f'd0_med2_neighbor{i}' for i in range(1, n_neighbors+1)]
neigh_d1_cols_2 = [f'd1_med2_neighbor{i}' for i in range(1, n_neighbors+1)]
neigh_cos0_cols_2 = [f'cos0_med2_neighbor{i}' for i in range(1, n_neighbors+1)]
neigh_all_cols_2 = neigh_d0_cols_2 + neigh_d1_cols_2
neigh_cols_2 = ['id'] + neigh_all_cols_2<load_from_csv>
class OptimizedRounder(object):
def __init__(self):
self.coef_ = 0
def _kappa_loss(self, coef, X, y):
X_p = np.copy(X)
 for i, pred in enumerate(X_p):
if pred < coef[0]:
X_p[i] = 0
elif pred >= coef[0] and pred < coef[1]:
X_p[i] = 1
elif pred >= coef[1] and pred < coef[2]:
X_p[i] = 2
elif pred >= coef[2] and pred < coef[3]:
X_p[i] = 3
else:
X_p[i] = 4
ll = quadratic_weighted_kappa(y, X_p)
 return -ll
def fit(self, X, y):
loss_partial = partial(self._kappa_loss, X=X, y=y)
 initial_coef = [0.5, 1.5, 2.5, 3.5]
self.coef_ = sp.optimize.minimize(loss_partial, initial_coef, method='nelder-mead')
 def predict(self, X, coef):
X_p = np.copy(X)
 for i, pred in enumerate(X_p):
if pred < coef[0]:
X_p[i] = 0
elif pred >= coef[0] and pred < coef[1]:
X_p[i] = 1
elif pred >= coef[1] and pred < coef[2]:
X_p[i] = 2
elif pred >= coef[2] and pred < coef[3]:
X_p[i] = 3
else:
X_p[i] = 4
return X_p
def coefficients(self):
return self.coef_['x']
df_train_neigh_files = [
'top-5_atoms-['med2']_count-1164536_0a918683-2fe3-43a1-b239-c900712c1433.csv',
'top-5_atoms-['med2']_count-1164536_b845e873-855d-424b-a7f3-50d89824b0ea.csv',
'top-5_atoms-['med2']_count-1164536_df446883-d0af-486c-877b-f8d834c0b00f.csv',
'top-5_atoms-['med2']_count-1164539_de6e2745-1a72-490c-ac29-69ad07c56910.csv',
]
df_neigh = load_frames(df_train_neigh_files, neigh_cols_2 )<merge>
def rmse(actual, predicted):
return sqrt(mean_squared_error(actual, predicted))
df_train = df_train.merge(df_neigh, on='id' )<sort_values>
%%time
print('Train')
 train = pd.read_csv(".. /input/train/train.csv")
 print(train.shape)
 print('Test')
 test = pd.read_csv(".. /input/test/test.csv")
 print(test.shape)
 print('Breeds')
 breeds = pd.read_csv(".. /input/breed_labels.csv")
 print(breeds.shape)
 print('Colors')
 colors = pd.read_csv(".. /input/color_labels.csv")
 print(colors.shape)
 print('States')
 states = pd.read_csv(".. /input/state_labels.csv")
 print(states.shape )
df_train.set_index('id', inplace=True)
 df_train.sort_index(inplace=True )<count_values>
target = train['AdoptionSpeed']
train_id = train['PetID']
test_id = test['PetID']
train.drop(['AdoptionSpeed', 'PetID'], axis=1, inplace=True)
 test.drop(['PetID'], axis=1, inplace=True )
df_train['atom_0'].value_counts() , df_train['atom_1'].value_counts()<drop_column>
%%time
doc_sent_mag = []
doc_sent_score = []
nf_count = 0
for pet in train_id:
try:
with open('.. /input/train_sentiment/' + pet + '.json', 'r')as f:
sentiment = json.load(f)
 doc_sent_mag.append(sentiment['documentSentiment']['magnitude'])
 doc_sent_score.append(sentiment['documentSentiment']['score'])
 except FileNotFoundError:
nf_count += 1
doc_sent_mag.append(-1)
 doc_sent_score.append(-1)
 train.loc[:, 'doc_sent_mag'] = doc_sent_mag
train.loc[:, 'doc_sent_score'] = doc_sent_score
doc_sent_mag = []
doc_sent_score = []
nf_count = 0
for pet in test_id:
try:
with open('.. /input/test_sentiment/' + pet + '.json', 'r')as f:
sentiment = json.load(f)
 doc_sent_mag.append(sentiment['documentSentiment']['magnitude'])
 doc_sent_score.append(sentiment['documentSentiment']['score'])
 except FileNotFoundError:
nf_count += 1
doc_sent_mag.append(-1)
 doc_sent_score.append(-1)
 test.loc[:, 'doc_sent_mag'] = doc_sent_mag
test.loc[:, 'doc_sent_score'] = doc_sent_score
df_train = df_train.drop(['atom_0'], axis=1 )<merge>
%%time
train_desc = train.Description.fillna("none" ).values
test_desc = test.Description.fillna("none" ).values
tfv = TfidfVectorizer(min_df=2, max_features=None,
strip_accents='unicode', analyzer='word', token_pattern=r'(?u)\b\w+\b',
ngram_range=(1, 3), use_idf=1, smooth_idf=1, sublinear_tf=1, 
)
 tfv.fit(list(train_desc))
X = tfv.transform(train_desc)
 X_test = tfv.transform(test_desc)
 svd = TruncatedSVD(n_components=200)
 svd.fit(X)
 print(svd.explained_variance_ratio_.sum())
 print(svd.explained_variance_ratio_)
 X = svd.transform(X)
 X = pd.DataFrame(X, columns=['svd_{}'.format(i)for i in range(120)])
 train = pd.concat(( train, X), axis=1)
 X_test = svd.transform(X_test)
 X_test = pd.DataFrame(X_test, columns=['svd_{}'.format(i)for i in range(120)])
 test = pd.concat(( test, X_test), axis=1 )
df_test = df_test.merge(df_struct,
left_on=['molecule_name','atom_index_0'],
right_on=['molecule_name','atom_index'],
sort=False )<merge>
%%time
vertex_xs = []
vertex_ys = []
bounding_confidences = []
bounding_importance_fracs = []
dominant_blues = []
dominant_greens = []
dominant_reds = []
dominant_pixel_fracs = []
dominant_scores = []
label_descriptions = []
label_scores = []
nf_count = 0
nl_count = 0
for pet in train_id:
try:
with open('.. /input/train_metadata/' + pet + '-1.json', 'r')as f:
data = json.load(f)
 vertex_x = data['cropHintsAnnotation']['cropHints'][0]['boundingPoly']['vertices'][2]['x']
vertex_xs.append(vertex_x)
 vertex_y = data['cropHintsAnnotation']['cropHints'][0]['boundingPoly']['vertices'][2]['y']
vertex_ys.append(vertex_y)
 bounding_confidence = data['cropHintsAnnotation']['cropHints'][0]['confidence']
bounding_confidences.append(bounding_confidence)
 bounding_importance_frac = data['cropHintsAnnotation']['cropHints'][0].get('importanceFraction', -1)
 bounding_importance_fracs.append(bounding_importance_frac)
 dominant_blue = data['imagePropertiesAnnotation']['dominantColors']['colors'][0]['color']['blue']
dominant_blues.append(dominant_blue)
 dominant_green = data['imagePropertiesAnnotation']['dominantColors']['colors'][0]['color']['green']
dominant_greens.append(dominant_green)
 dominant_red = data['imagePropertiesAnnotation']['dominantColors']['colors'][0]['color']['red']
dominant_reds.append(dominant_red)
 dominant_pixel_frac = data['imagePropertiesAnnotation']['dominantColors']['colors'][0]['pixelFraction']
dominant_pixel_fracs.append(dominant_pixel_frac)
 dominant_score = data['imagePropertiesAnnotation']['dominantColors']['colors'][0]['score']
dominant_scores.append(dominant_score)
 if data.get('labelAnnotations'):
label_description = data['labelAnnotations'][0]['description']
label_descriptions.append(label_description)
 label_score = data['labelAnnotations'][0]['score']
label_scores.append(label_score)
 else:
nl_count += 1
label_descriptions.append('nothing')
 label_scores.append(-1)
 except FileNotFoundError:
nf_count += 1
vertex_xs.append(-1)
 vertex_ys.append(-1)
 bounding_confidences.append(-1)
 bounding_importance_fracs.append(-1)
 dominant_blues.append(-1)
 dominant_greens.append(-1)
 dominant_reds.append(-1)
 dominant_pixel_fracs.append(-1)
 dominant_scores.append(-1)
 label_descriptions.append('nothing')
 label_scores.append(-1)
 print(nf_count)
 print(nl_count)
 train.loc[:, 'vertex_x'] = vertex_xs
train.loc[:, 'vertex_y'] = vertex_ys
train.loc[:, 'bounding_confidence'] = bounding_confidences
train.loc[:, 'bounding_importance'] = bounding_importance_fracs
train.loc[:, 'dominant_blue'] = dominant_blues
train.loc[:, 'dominant_green'] = dominant_greens
train.loc[:, 'dominant_red'] = dominant_reds
train.loc[:, 'dominant_pixel_frac'] = dominant_pixel_fracs
train.loc[:, 'dominant_score'] = dominant_scores
train.loc[:, 'label_description'] = label_descriptions
train.loc[:, 'label_score'] = label_scores
vertex_xs = []
vertex_ys = []
bounding_confidences = []
bounding_importance_fracs = []
dominant_blues = []
dominant_greens = []
dominant_reds = []
dominant_pixel_fracs = []
dominant_scores = []
label_descriptions = []
label_scores = []
nf_count = 0
nl_count = 0
for pet in test_id:
try:
with open('.. /input/test_metadata/' + pet + '-1.json', 'r')as f:
data = json.load(f)
 vertex_x = data['cropHintsAnnotation']['cropHints'][0]['boundingPoly']['vertices'][2]['x']
vertex_xs.append(vertex_x)
 vertex_y = data['cropHintsAnnotation']['cropHints'][0]['boundingPoly']['vertices'][2]['y']
vertex_ys.append(vertex_y)
 bounding_confidence = data['cropHintsAnnotation']['cropHints'][0]['confidence']
bounding_confidences.append(bounding_confidence)
 bounding_importance_frac = data['cropHintsAnnotation']['cropHints'][0].get('importanceFraction', -1)
 bounding_importance_fracs.append(bounding_importance_frac)
 dominant_blue = data['imagePropertiesAnnotation']['dominantColors']['colors'][0]['color']['blue']
dominant_blues.append(dominant_blue)
 dominant_green = data['imagePropertiesAnnotation']['dominantColors']['colors'][0]['color']['green']
dominant_greens.append(dominant_green)
 dominant_red = data['imagePropertiesAnnotation']['dominantColors']['colors'][0]['color']['red']
dominant_reds.append(dominant_red)
 dominant_pixel_frac = data['imagePropertiesAnnotation']['dominantColors']['colors'][0]['pixelFraction']
dominant_pixel_fracs.append(dominant_pixel_frac)
 dominant_score = data['imagePropertiesAnnotation']['dominantColors']['colors'][0]['score']
dominant_scores.append(dominant_score)
 if data.get('labelAnnotations'):
label_description = data['labelAnnotations'][0]['description']
label_descriptions.append(label_description)
 label_score = data['labelAnnotations'][0]['score']
label_scores.append(label_score)
 else:
nl_count += 1
label_descriptions.append('nothing')
 label_scores.append(-1)
 except FileNotFoundError:
nf_count += 1
vertex_xs.append(-1)
 vertex_ys.append(-1)
 bounding_confidences.append(-1)
 bounding_importance_fracs.append(-1)
 dominant_blues.append(-1)
 dominant_greens.append(-1)
 dominant_reds.append(-1)
 dominant_pixel_fracs.append(-1)
 dominant_scores.append(-1)
 label_descriptions.append('nothing')
 label_scores.append(-1)
 print(nf_count)
 test.loc[:, 'vertex_x'] = vertex_xs
test.loc[:, 'vertex_y'] = vertex_ys
test.loc[:, 'bounding_confidence'] = bounding_confidences
test.loc[:, 'bounding_importance'] = bounding_importance_fracs
test.loc[:, 'dominant_blue'] = dominant_blues
test.loc[:, 'dominant_green'] = dominant_greens
test.loc[:, 'dominant_red'] = dominant_reds
test.loc[:, 'dominant_pixel_frac'] = dominant_pixel_fracs
test.loc[:, 'dominant_score'] = dominant_scores
test.loc[:, 'label_description'] = label_descriptions
test.loc[:, 'label_score'] = label_scores
df_test = df_test.merge(df_struct,
left_on=['molecule_name','atom_index_1'],
right_on=['molecule_name','atom_index'],
suffixes=('_0', '_1'),
sort=False )<load_from_csv>
%%time
train.drop(['Name', 'RescuerID', 'Description'], axis=1, inplace=True)
 test.drop(['Name', 'RescuerID', 'Description'], axis=1, inplace=True )
df_test_neigh_files = [
'top-5_atoms-['sep2']_count-626385_7623bd50-41b8-4c44-b40a-e37837700460.csv',
'top-5_atoms-['sep2']_count-626385_878ae0ca-67c7-491e-b580-fbdc384c2477.csv',
'top-5_atoms-['sep2']_count-626385_e4f75a2d-302c-476b-b60c-c5624925ef01.csv',
'top-5_atoms-['sep2']_count-626387_4792052c-d539-4e62-8e37-c4ecf81ef669.csv',
]
df_neigh = load_frames(df_test_neigh_files, neigh_cols )<merge>
numeric_cols = ['Age', 'Quantity', 'Fee', 'VideoAmt', 'PhotoAmt', 'AdoptionSpeed', 'doc_sent_mag', 'doc_sent_score', 'dominant_score', 'dominant_pixel_frac', 'dominant_red', 'dominant_green', 'dominant_blue', 'bounding_importance', 'bounding_confidence', 'vertex_x', 'vertex_y', 'label_score'] + ['svd_{}'.format(i)for i in range(120)]
cat_cols = list(set(train.columns)- set(numeric_cols))
train.loc[:, cat_cols] = train[cat_cols].astype('category')
 test.loc[:, cat_cols] = test[cat_cols].astype('category')
 print(train.shape)
 print(test.shape)
 train.head()
df_test = df_test.merge(df_neigh, on='id' )<load_from_csv>
def run_cv_model(train, test, target, model_fn, params={}, eval_fn=None, label='model'):
kf = StratifiedKFold(n_splits=5, random_state=42, shuffle=True)
 fold_splits = kf.split(train, target)
 cv_scores = []
qwk_scores = []
pred_full_test = 0
pred_train = np.zeros(( train.shape[0], 5))
all_coefficients = np.zeros(( 5, 4))
feature_importance_df = pd.DataFrame()
i = 1
for dev_index, val_index in fold_splits:
print('Started ' + label + ' fold ' + str(i)+ '/5')
 if isinstance(train, pd.DataFrame):
dev_X, val_X = train.iloc[dev_index], train.iloc[val_index]
dev_y, val_y = target[dev_index], target[val_index]
else:
dev_X, val_X = train[dev_index], train[val_index]
dev_y, val_y = target[dev_index], target[val_index]
params2 = params.copy()
pred_val_y, pred_test_y, importances, coefficients, qwk = model_fn(dev_X, dev_y, val_X, val_y, test, params2)
 pred_full_test = pred_full_test + pred_test_y
pred_train[val_index] = pred_val_y
all_coefficients[i-1, :] = coefficients
if eval_fn is not None:
cv_score = eval_fn(val_y, pred_val_y)
 cv_scores.append(cv_score)
 qwk_scores.append(qwk)
 print(label + ' cv score {}: RMSE {} QWK {}'.format(i, cv_score, qwk))
fold_importance_df = pd.DataFrame()
fold_importance_df['feature'] = train.columns.values
fold_importance_df['importance'] = importances
fold_importance_df['fold'] = i
feature_importance_df = pd.concat([feature_importance_df, fold_importance_df], axis=0)
 i += 1
print('{} cv RMSE scores : {}'.format(label, cv_scores))
print('{} cv mean RMSE score : {}'.format(label, np.mean(cv_scores)))
 print('{} cv std RMSE score : {}'.format(label, np.mean(cv_scores)))
 print('{} cv QWK scores : {}'.format(label, qwk_scores))
print('{} cv mean QWK score : {}'.format(label, np.mean(qwk_scores)))
 print('{} cv std QWK score : {}'.format(label, np.std(qwk_scores)))
 pred_full_test = pred_full_test / 5.0
results = {'label': label,
'train': pred_train, 'test': pred_full_test,
'cv': cv_scores, 'qwk': qwk_scores,
'importance': feature_importance_df,
'coefficients': all_coefficients}
return results
params = {'application': 'regression',
'boosting': 'gbdt',
'metric': 'rmse',
'num_leaves': 80,
'max_depth': 11,
'learning_rate': 0.01,
'bagging_fraction': 0.9,
'feature_fraction': 0.8,
'min_split_gain': 0.01,
'min_child_samples': 10,
'min_child_weight': 0.1,
'verbosity': -1,
'data_random_seed': 3,
'early_stop': 100,
'verbose_eval': 100,
'num_rounds': 10000}
def runLGB(train_X, train_y, test_X, test_y, test_X2, params):
print('Prep LGB')
 d_train = lgb.Dataset(train_X, label=train_y)
 d_valid = lgb.Dataset(test_X, label=test_y)
 watchlist = [d_train, d_valid]
print('Train LGB')
 num_rounds = params.pop('num_rounds')
 verbose_eval = params.pop('verbose_eval')
 early_stop = None
if params.get('early_stop'):
early_stop = params.pop('early_stop')
 model = lgb.train(params,
train_set=d_train,
num_boost_round=num_rounds,
valid_sets=watchlist,
verbose_eval=verbose_eval,
early_stopping_rounds=early_stop)
 print('Predict 1/2')
 pred_test_y = model.predict(test_X, num_iteration=model.best_iteration)
 optR = OptimizedRounder()
optR.fit(pred_test_y, test_y)
 coefficients = optR.coefficients()
pred_test_y_k = optR.predict(pred_test_y, coefficients)
 print("Valid Counts = ", Counter(test_y))
print("Predicted Counts = ", Counter(pred_test_y_k))
print("Coefficients = ", coefficients)
 qwk = quadratic_weighted_kappa(test_y, pred_test_y_k)
 print("QWK = ", qwk)
 print('Predict 2/2')
 pred_test_y2 = model.predict(test_X2, num_iteration=model.best_iteration)
 return pred_test_y.reshape(-1, 1), pred_test_y2.reshape(-1, 1), model.feature_importance() , coefficients, qwk
results = run_cv_model(train, test, target, runLGB, params, rmse, 'lgb' )
df_test_neigh_files = [
'top-5_atoms-['med2']_count-626385_0072225a-03f6-42bf-b1ed-b81be41c6d14.csv',
'top-5_atoms-['med2']_count-626385_7ed7d305-6c6a-4d3c-a6e8-f8e274fd461f.csv',
'top-5_atoms-['med2']_count-626385_b4f9dd09-7c61-42a7-9534-85a31496d1a5.csv',
'top-5_atoms-['med2']_count-626387_a9d566a5-1248-4341-abec-b71a87bbe21d.csv',
]
df_neigh = load_frames(df_test_neigh_files, neigh_cols_2 )<merge>
optR = OptimizedRounder()
coefficients_ = np.mean(results['coefficients'], axis=0)
 print(coefficients_)
 train_predictions = [r[0] for r in results['train']]
train_predictions = optR.predict(train_predictions, coefficients_ ).astype(int)
 Counter(train_predictions )
df_test = df_test.merge(df_neigh, on='id' )<sort_values>
optR = OptimizedRounder()
test_predictions = [r[0] for r in results['test']]
test_predictions = optR.predict(test_predictions, coefficients_ ).astype(int)
 Counter(test_predictions )
df_test.set_index('id', inplace=True)
 df_test.sort_index(inplace=True )<count_values>
pd.DataFrame(sk_cmatrix(target, train_predictions), index=list(range(5)) , columns=list(range(5)) )
df_test['atom_0'].value_counts() , df_test['atom_1'].value_counts()<drop_column>
quadratic_weighted_kappa(target, train_predictions )
df_test = df_test.drop(['atom_0'], axis=1 )<drop_column>
rmse(target, [r[0] for r in results['train']] )
del(df_struct)
 del(df_neigh )<compute_test_metric>
submission = pd.DataFrame({'PetID': test_id, 'AdoptionSpeed': test_predictions})
 submission.head()
def calc_polar(df, suffix_0='_0', suffix_1='_1', angles=True):

 dx = df['x'+suffix_0] - df['x'+suffix_1]
dy = df['y'+suffix_0] - df['y'+suffix_1]
dz = df['z'+suffix_0] - df['z'+suffix_1]
if angles:
dx2 = np.power(dx, 2)
 dy2 = np.power(dy, 2)
 dz2 = np.power(dz, 2)
 d = np.sqrt(dx2 + dy2 + dz2)
 az = np.arctan2(dy, dx)
 el = np.arctan2(dz, np.sqrt(dx2 + dy2))
return d, az, el
else:
return np.sqrt(dx**2 + dy**2 + dz**2 )<categorify>
submission.to_csv('submission.csv', index=False )
def feat_eng(df):
d, _, _ = calc_polar(df)
 df_dummy = pd.get_dummies(df[['atom_1']], columns=['atom_1'])
 atom_1_cols = [x for x in df_dummy.columns if 'atom_1' in x]
d_cols = [f'd_{x[7:]}' for x in atom_1_cols]
df[d_cols] = df_dummy[atom_1_cols].multiply(d, axis='index' )<import_modules>
submission.to_csv('submission.csv', index=False )