Kaggle competition: Flower Classification with TPUs
<SOS> metric: MacroFScore Kaggle data source: flower-classification-with-tpus<train_model>
!pip install -q efficientnet
learning_print(best_ridge, butch_train, target, kfolds )<train_on_grid>
print('TensorFlow version', tf.__version__)
 AUTO = tf.data.experimental.AUTOTUNE
pipe = Pipeline([scl,('lasso', Lasso(max_iter=2000)) ])
 param_grid = [{'lasso__alpha' : np.arange(0.0001,0.001, 0.00001)}]
grid = GridSearchCV(pipe, param_grid=param_grid,
cv=kfolds, scoring='neg_mean_squared_error', return_train_score=True, n_jobs=-1)
 %time grid.fit(butch_train, target)
 best_lasso = grid.best_estimator_
print(best_lasso)
 print("_"*40)
 print(np.sqrt(-grid.best_score_))<choose_model_class>
dol = get_dollars(best_lasso, kfolds, butch_train, target, old_target)
 dol<train_on_grid>
start_time = datetime.now()
print('Time now is', start_time)
 end_training_by_tdelta = timedelta(seconds=8400)
 this_run_file_prefix = start_time.strftime('%Y%m%d_%H%M_')
 print(this_run_file_prefix)
 IMAGE_SIZE = [224, 224]
EPOCHS = 12
BATCH_SIZE = 16 * strategy.num_replicas_in_sync
GCS_PATH_SELECT = {
192: GCS_DS_PATH + '/tfrecords-jpeg-192x192',
224: GCS_DS_PATH + '/tfrecords-jpeg-224x224',
331: GCS_DS_PATH + '/tfrecords-jpeg-331x331',
512: GCS_DS_PATH + '/tfrecords-jpeg-512x512'
}
GCS_PATH = GCS_PATH_SELECT[IMAGE_SIZE[0]]
TRAINING_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/train/*.tfrec')
 VALIDATION_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/val/*.tfrec')
 TEST_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/test/*.tfrec')
 CLASSES = ['pink primrose', 'hard-leaved pocket orchid', 'canterbury bells', 'sweet pea', 'wild geranium', 'tiger lily', 'moon orchid', 'bird of paradise', 'monkshood', 'globe thistle',
'snapdragon', "colt's foot", 'king protea', 'spear thistle', 'yellow iris', 'globe-flower', 'purple coneflower', 'peruvian lily', 'balloon flower', 'giant white arum lily',
'fire lily', 'pincushion flower', 'fritillary', 'red ginger', 'grape hyacinth', 'corn poppy', 'prince of wales feathers', 'stemless gentian', 'artichoke', 'sweet william',
'carnation', 'garden phlox', 'love in the mist', 'cosmos', 'alpine sea holly', 'ruby-lipped cattleya', 'cape flower', 'great masterwort', 'siam tulip', 'lenten rose',
'barberton daisy', 'daffodil', 'sword lily', 'poinsettia', 'bolero deep blue', 'wallflower', 'marigold', 'buttercup', 'daisy', 'common dandelion',
'petunia', 'wild pansy', 'primula', 'sunflower', 'lilac hibiscus', 'bishop of llandaff', 'gaura', 'geranium', 'orange dahlia', 'pink-yellow dahlia',
'cautleya spicata', 'japanese anemone', 'black-eyed susan', 'silverbush', 'californian poppy', 'osteospermum', 'spring crocus', 'iris', 'windflower', 'tree poppy',
'gazania', 'azalea', 'water lily', 'rose', 'thorn apple', 'morning glory', 'passion flower', 'lotus', 'toad lily', 'anthurium',
'frangipani', 'clematis', 'hibiscus', 'columbine', 'desert-rose', 'tree mallow', 'magnolia', 'cyclamen ', 'watercress', 'canna lily',
'hippeastrum ', 'bee balm', 'pink quill', 'foxglove', 'bougainvillea', 'camellia', 'mallow', 'mexican petunia', 'bromelia', 'blanket flower',
'trumpet creeper', 'blackberry lily', 'common tulip', 'wild rose']
pipe = Pipeline([scl,('ridge', Ridge())])
 param_grid = [{'ridge__alpha' : np.arange(5,15,0.5)}]
grid = GridSearchCV(pipe, param_grid=param_grid,
cv=kfolds, scoring='neg_mean_squared_error', return_train_score=True, n_jobs=-1)
 %time grid.fit(butch_train, target)
 best_ridge = grid.best_estimator_
print(best_ridge)
 print("_"*40)
 print(np.sqrt(-grid.best_score_))<create_dataframe>
np.set_printoptions(threshold=15, linewidth=80)
 def batch_to_numpy_images_and_labels(data):
images, labels = data
numpy_images = images.numpy()
numpy_labels = labels.numpy()
if numpy_labels.dtype == object:
numpy_labels = [None for _ in enumerate(numpy_images)]
return numpy_images, numpy_labels
def title_from_label_and_target(label, correct_label):
if correct_label is None:
return CLASSES[label], True
correct =(label == correct_label)
 return "{} [{}{}{}]".format(CLASSES[label], 'OK' if correct else 'NO', u"\u2192" if not correct else '',
CLASSES[correct_label] if not correct else ''), correct
def display_one_flower(image, title, subplot, red=False, titlesize=16):
plt.subplot(*subplot)
 plt.axis('off')
 plt.imshow(image)
 if len(title)> 0:
plt.title(title, fontsize=int(titlesize)if not red else int(titlesize/1.2), color='red' if red else 'black', fontdict={'verticalalignment':'center'}, pad=int(titlesize/1.5))
return(subplot[0], subplot[1], subplot[2]+1)
 def display_batch_of_images(databatch, predictions=None):

 images, labels = batch_to_numpy_images_and_labels(databatch)
 if labels is None:
labels = [None for _ in enumerate(images)]
rows = int(math.sqrt(len(images)))
 cols = len(images)//rows
FIGSIZE = 13.0
SPACING = 0.1
subplot=(rows,cols,1)
 if rows < cols:
plt.figure(figsize=(FIGSIZE,FIGSIZE/cols*rows))
else:
plt.figure(figsize=(FIGSIZE/rows*cols,FIGSIZE))
for i,(image, label)in enumerate(zip(images[:rows*cols], labels[:rows*cols])) :
title = '' if label is None else CLASSES[label]
correct = True
if predictions is not None:
title, correct = title_from_label_and_target(predictions[i], label)
 dynamic_titlesize = FIGSIZE*SPACING/max(rows,cols)*40+3
subplot = display_one_flower(image, title, subplot, not correct, titlesize=dynamic_titlesize)
 plt.tight_layout()
if label is None and predictions is None:
plt.subplots_adjust(wspace=0, hspace=0)
 else:
plt.subplots_adjust(wspace=SPACING, hspace=SPACING)
 plt.show()
def display_confusion_matrix(cmat, score, precision, recall):
plt.figure(figsize=(15,15))
ax = plt.gca()
ax.matshow(cmat, cmap='Reds')
 ax.set_xticks(range(len(CLASSES)))
 ax.set_xticklabels(CLASSES, fontdict={'fontsize': 7})
 plt.setp(ax.get_xticklabels() , rotation=45, ha="left", rotation_mode="anchor")
 ax.set_yticks(range(len(CLASSES)))
 ax.set_yticklabels(CLASSES, fontdict={'fontsize': 7})
 plt.setp(ax.get_yticklabels() , rotation=45, ha="right", rotation_mode="anchor")
 titlestring = ""
if score is not None:
titlestring += 'f1 = {:.3f} '.format(score)
 if precision is not None:
titlestring += '
precision = {:.3f} '.format(precision)
 if recall is not None:
titlestring += '
recall = {:.3f} '.format(recall)
 if len(titlestring)> 0:
ax.text(101, 1, titlestring, fontdict={'fontsize': 18, 'horizontalalignment':'right', 'verticalalignment':'top', 'color':'
plt.show()
def display_training_curves(training, validation, title, subplot):
if subplot%10==1:
plt.subplots(figsize=(10,10), facecolor='
plt.tight_layout()
ax = plt.subplot(subplot)
 ax.set_facecolor('
ax.plot(training)
 ax.plot(validation)
 ax.set_title('model '+ title)
 ax.set_ylabel(title)
 ax.set_xlabel('epoch')
 ax.legend(['train', 'valid.'] )
scoresummary = pd.DataFrame({'Model Name' : modelname,
'CVScore Lasso': lassoscore,
'CVScore Ridge': ridgescore,
'KaggleScore Lasso': kaggle_lasso,
'KaggleScore Ridge': kaggle_ridge,
'MAE Lasso': maecv_lasso,
'MAE Ridge': maecv_ridge,
'Cumulative Time': timeelapsed})
 scoresummary[['Model Name', 'CVScore Lasso', 'CVScore Ridge', 'KaggleScore Lasso',
'KaggleScore Ridge', 'MAE Lasso', 'MAE Ridge', 'Cumulative Time']]<set_options>
def decode_image(image_data):
image = tf.image.decode_jpeg(image_data, channels = 3)
 image = tf.cast(image, tf.float32)/ 255.0
image = tf.reshape(image, [*IMAGE_SIZE, 3])
 return image
def read_labeled_tfrecord(example):
LABELED_TFREC_FORMAT = {
'image': tf.io.FixedLenFeature([], tf.string),
'class': tf.io.FixedLenFeature([], tf.int64),
}
example = tf.io.parse_single_example(example, LABELED_TFREC_FORMAT)
 image = decode_image(example['image'])
 label = tf.cast(example['class'], tf.int32)
 return image, label
def read_unlabeled_tfrecord(example):
UNLABELED_TFREC_FORMAT = {
'image': tf.io.FixedLenFeature([], tf.string),
'id': tf.io.FixedLenFeature([], tf.string),
}
example = tf.io.parse_single_example(example, UNLABELED_TFREC_FORMAT)
 image = decode_image(example['image'])
 idnum = example['id']
return image, idnum
def load_dataset(filenames, labeled = True, ordered = False):
ignore_order = tf.data.Options()
if not ordered:
ignore_order.experimental_deterministic = False
dataset = tf.data.TFRecordDataset(filenames, num_parallel_reads = AUTO)
 dataset = dataset.with_options(ignore_order)
 dataset = dataset.map(read_labeled_tfrecord if labeled else read_unlabeled_tfrecord, num_parallel_calls = AUTO)
 return dataset
def data_augment(image, label):
image = tf.image.random_flip_left_right(image)
 image = tf.image.random_saturation(image, 0, 2)
 return image, label
def get_training_dataset() :
dataset = load_dataset(TRAINING_FILENAMES, labeled = True)
 dataset = dataset.map(data_augment, num_parallel_calls = AUTO)
 dataset = dataset.repeat()
dataset = dataset.shuffle(2048)
 dataset = dataset.batch(BATCH_SIZE)
 dataset = dataset.prefetch(AUTO)
 return dataset
def get_validation_dataset(ordered = False):
dataset = load_dataset(VALIDATION_FILENAMES, labeled = True, ordered=ordered)
 dataset = dataset.batch(BATCH_SIZE)
 dataset = dataset.cache()
dataset = dataset.prefetch(AUTO)
 return dataset
def get_test_dataset(ordered = False):
dataset = load_dataset(TEST_FILENAMES, labeled = False, ordered = ordered)
 dataset = dataset.batch(BATCH_SIZE)
 dataset = dataset.prefetch(AUTO)
 return dataset
def count_data_items(filenames):
n = [int(re.compile(r"-([0-9]*)\." ).search(filename ).group(1)) for filename in filenames]
return np.sum(n)
 NUM_TRAINING_IMAGES = count_data_items(TRAINING_FILENAMES)
 NUM_VALIDATION_IMAGES = count_data_items(VALIDATION_FILENAMES)
 NUM_TEST_IMAGES = count_data_items(TEST_FILENAMES)
 STEPS_PER_EPOCH = NUM_TRAINING_IMAGES // BATCH_SIZE
print('Dataset: {} training images, {} validation images, {} unlabeled test images'.format(NUM_TRAINING_IMAGES, NUM_VALIDATION_IMAGES, NUM_TEST_IMAGES))
warnings.filterwarnings('ignore')
 %matplotlib inline
pd.set_option('display.max_columns', 300 )<load_from_csv>
df_train = pd.read_csv('.. /input/train.csv', index_col="Id")
 df_test = pd.read_csv('.. /input/test.csv', index_col="Id")
 y_train = df_train.SalePrice
df_train = df_train.drop(columns='SalePrice')
 print(len(df_train._get_numeric_data().columns))
print(len(df_train.select_dtypes(include='object' ).columns))<data_type_conversions>
test_ds = get_test_dataset(ordered = True)
 test_images_ds = test_ds.map(lambda image, idnum: image )
categorical_features = df_train.select_dtypes(include='object' ).columns
df_train[categorical_features] = df_train[categorical_features].fillna('None')
 df_test[categorical_features] = df_test[categorical_features].fillna('None' )<categorify>
start_time = datetime.now()
this_run_file_prefix = start_time.strftime('%Y%m%d_%H%M_')
 start_model = 0
model_indx_0 = start_model
model_indx_1 = start_model + 1
test_vals_pickle_outputs = ['/kaggle/input/all-previous-4/97731.pkl',
'/kaggle/input/all-previous-4/97606.pkl',
'/kaggle/input/all-previous-4/97611.pkl',
'/kaggle/input/all-previous-4/97659.pkl']
df_train["LotFrontage"] = df_train.groupby("Neighborhood")["LotFrontage"].transform(
 lambda x: x.fillna(x.median()))
df_test["LotFrontage"] = df_test.groupby("Neighborhood")["LotFrontage"].transform(
 lambda x: x.fillna(x.median()))
numerical_features = df_train.select_dtypes(include='number' ).columns
my_imputer = Imputer()
df_train[numerical_features] = my_imputer.fit_transform(df_train[numerical_features])
 df_test[numerical_features] = my_imputer.transform(df_test[numerical_features])
<concatenate>
def get_results_from_pickle(filename):
pklfile = open(filename, 'rb')
 test_results = pickle.load(pklfile)
 pklfile.close()
return test_results
idx_split = df_train.shape[0]
df_train = df_train.append(df_test )<data_type_conversions>
no_of_models = len(test_vals_pickle_outputs)
 cm_correct_labels_results = [0] * no_of_models
cm_predictions_results = [0] * no_of_models
test_ids_results = [0] * no_of_models
val_probabilities = [0] * no_of_models
test_probabilities = [0] * no_of_models
for i in range(no_of_models):
test_results = get_results_from_pickle(test_vals_pickle_outputs[i])
 [cm_correct_labels_results[i], cm_predictions_results[i], test_ids_results[i], val_probabilities[i], test_probabilities[i]] = test_results
NumStr = ["MSSubClass","BsmtFullBath","BsmtHalfBath","HalfBath","BedroomAbvGr","KitchenAbvGr","MoSold","YrSold","YearBuilt","YearRemodAdd","LowQualFinSF","GarageYrBlt"]
for col in NumStr:
df_train[col]=df_train[col].astype(str)
 non_ordinal_features = ['LandSlope','Utilities','MSZoning', 'Street','Alley','LotShape','LandContour','LotConfig','Neighborhood','Condition1','Condition2','BldgType','HouseStyle', 'RoofStyle','RoofMatl','Exterior1st', 'Exterior2nd','MasVnrType','Foundation', 'BsmtExposure','BsmtFinType1','BsmtFinType2', 'Heating', 'CentralAir', 'Electrical','Functional', 'GarageType','GarageFinish','PavedDrive', 'Fence','MiscFeature','SaleType','SaleCondition', "MSSubClass","BsmtFullBath","BsmtHalfBath","HalfBath","BedroomAbvGr","KitchenAbvGr","MoSold","YrSold","YearBuilt","YearRemodAdd","LowQualFinSF","GarageYrBlt"]
ordinal_features = df_train.select_dtypes(include='object' ).columns.drop(non_ordinal_features )<categorify>
cm_probabilities = np.zeros(( val_probabilities[0].shape))
for j in range(no_of_models):
cm_probabilities = cm_probabilities + val_probabilities[j]
cm_predictions = np.argmax(cm_probabilities, axis = -1)
 print('Correct labels: ', cm_correct_labels_results[0].shape, cm_correct_labels_results[0])
 print('Predicted labels: ', cm_predictions.shape, cm_predictions )
dummies = pd.get_dummies(df_train.loc[:,non_ordinal_features], drop_first=True)
 df_train = pd.concat([df_train,dummies], axis=1)
 df_train = df_train.drop(non_ordinal_features,axis=1 )<categorify>
def getFitPrecisionRecall(correct_labels, predictions):
score = f1_score(correct_labels, predictions, labels = range(len(CLASSES)) , average = 'macro')
 precision = precision_score(correct_labels, predictions, labels = range(len(CLASSES)) , average = 'macro')
 recall = recall_score(correct_labels, predictions, labels = range(len(CLASSES)) , average = 'macro')
 return score, precision, recall
def cat_to_num(x):
if x=='Ex':
return 5
if x=='Gd':
return 4
if x=='TA':
return 3
if x=='Fa':
return 2
if x=='Po':
return 1
if x=='None':
return 0
df_train.loc[:,ordinal_features] = df_train.loc[:,ordinal_features].applymap(cat_to_num )<prepare_x_and_y>
for j in range(no_of_models):
model_predictions = np.argmax(val_probabilities[j], axis = -1)
 score, precision, recall = getFitPrecisionRecall(cm_correct_labels_results[0], model_predictions)
 print('For model: {}, f1 score: {:.4f}, precision: {:.4f}, recall: {:.4f}'.format(j, score, precision, recall))
X = df_train.loc[:idx_split, :]
X_test = df_train.loc[idx_split+1:, :]<concatenate>
cmat = confusion_matrix(cm_correct_labels_results[0], cm_predictions, labels = range(len(CLASSES)))
 score, precision, recall = getFitPrecisionRecall(cm_correct_labels_results[0], cm_predictions)
 cmat =(cmat.T / cmat.sum(axis = -1)).T
display_confusion_matrix(cmat, score, precision, recall)
 print('f1 score: {:.6f}, precision: {:.6f}, recall: {:.6f}'.format(score, precision, recall))
X = X.assign(SalePrice=y_train )<drop_column>
def create_submission_file_not_the_right_way(filename, probabilities, test_ids):
predictions = np.argmax(probabilities, axis = -1)
 print('Generating submission file...', filename)
 np.savetxt(filename, np.rec.fromarrays([test_ids, predictions]), fmt = ['%s', '%d'], delimiter = ',', header = 'id,label', comments = '')
 def create_submission_file(filename, probabilities):
predictions = np.argmax(probabilities, axis = -1)
 print('Generating submission file...', filename)
 test_ids_ds = test_ds.map(lambda image, idnum: idnum ).unbatch()
test_ids = next(iter(test_ids_ds.batch(NUM_TEST_IMAGES)) ).numpy().astype('U')
 np.savetxt(filename, np.rec.fromarrays([test_ids, predictions]), fmt = ['%s', '%d'], delimiter = ',', header = 'id,label', comments = '')
X = X.drop(columns='SalePrice' )<feature_engineering>
def combine_two(correct_labels, probability_0, probability_1):
print('Start.', datetime.now())
 alphas0_to_try = np.linspace(0, 1, 101)
 best_score = -1
best_alpha0 = -1
best_alpha1 = -1
best_precision = -1
best_recall = -1
best_val_predictions = None
for alpha0 in alphas0_to_try:
alpha1 = 1.0 - alpha0
probabilities = alpha0 * probability_0 + alpha1 * probability_1
predictions = np.argmax(probabilities, axis = -1)
 score, precision, recall = getFitPrecisionRecall(correct_labels, predictions)
 if score > best_score:
best_alpha0 = alpha0
best_alpha1 = alpha1
best_score = score
best_precision = precision
best_recall = recall
best_val_predictions = predictions
return best_alpha0, best_alpha1, best_val_predictions, best_score, best_precision, best_recall
X["TotalHouse"] = X["TotalBsmtSF"] + X["1stFlrSF"] + X["2ndFlrSF"]
X["TotalHouse_OverallQual"] = X["TotalHouse"] * X["OverallQual"]
X["GrLivArea_OverallQual"] = X["GrLivArea"] * X["OverallQual"]
X_test["TotalHouse"] = X_test["TotalBsmtSF"] + X_test["1stFlrSF"] + X_test["2ndFlrSF"]
X_test["TotalHouse_OverallQual"] = X_test["TotalHouse"] * X_test["OverallQual"]
X_test["GrLivArea_OverallQual"] = X_test["GrLivArea"] * X_test["OverallQual"] 
<prepare_x_and_y>
def combine_three(correct_labels, probability_0, probability_1, probability_2):
print('Start.', datetime.now())
 alphas0_to_try = np.linspace(0, 1, 101)
 alphas1_to_try = np.linspace(0, 1, 101)
 best_score = -1
best_alpha0 = -1
best_alpha1 = -1
best_alpha2 = -1
best_precision = -1
best_recall = -1
best_val_predictions = None
for alpha0 in alphas0_to_try:
for alpha1 in alphas1_to_try:
if(alpha0 + alpha1)> 1.0:
break
alpha2 = 1.0 - alpha0 - alpha1
probabilities = alpha0 * probability_0 + alpha1 * probability_1 + alpha2 * probability_2
predictions = np.argmax(probabilities, axis = -1)
 score, precision, recall = getFitPrecisionRecall(correct_labels, predictions)
 if score > best_score:
best_alpha0 = alpha0
best_alpha1 = alpha1
best_alpha2 = alpha2
best_score = score
best_precision = precision
best_recall = recall
best_val_predictions = predictions
return best_alpha0, best_alpha1, best_alpha2, best_val_predictions, best_score, best_precision, best_recall
print(X[(X['GrLivArea']>4000)&(y_train<300000)].index)
 X = X.drop(X[(X['GrLivArea']>4000)&(y_train<300000)].index)
 y_train = y_train.drop([524, 1299] )<feature_engineering>
probabilities = np.zeros(( test_probabilities[0].shape))
for j in range(no_of_models):
probabilities = probabilities + test_probabilities[j]
create_submission_file_not_the_right_way('submission.csv', probabilities, test_ids_results[0])
numeric_feats = X.dtypes[X.dtypes != "object"].index
numeric_feats = numeric_feats.drop(dummies)
 numeric_feats = numeric_feats.drop(ordinal_features)
 skewed_feats = X[numeric_feats].apply(lambda x: skew(x.dropna())).sort_values(ascending=False)
 skewness = pd.DataFrame({'Skew' :skewed_feats})
 skewness = skewness[abs(skewness)> 0.75]
skewed_features = skewness.index
lam = 0.15
for feat in skewed_features:
X[feat] = boxcox1p(X[feat], lam)
 X_test[feat] = boxcox1p(X_test[feat], lam )<feature_engineering>
def get_best_combination(no_models, cm_correct_labels, val_probabilities, test_probabilities):
best_fit_score = -10000.0
best_predictions = 0
choose_filename = ''
curr_predictions = np.argmax(val_probabilities[0], axis = -1)
 score, precision, recall = getFitPrecisionRecall(cm_correct_labels, curr_predictions)
 print('f1 score: {:.3f}, precision: {:.3f}, recall: {:.3f}'.format(score, precision, recall))
filename = this_run_file_prefix + 'submission_0.csv'
if best_fit_score < score:
best_fit_score = score
best_predictions = curr_predictions
choose_filename = filename
create_submission_file('./submission.csv', test_probabilities[0])
 create_submission_file(filename, test_probabilities[0])
 if no_models > 1:
curr_predictions = np.argmax(val_probabilities[1], axis = -1)
 score, precision, recall = getFitPrecisionRecall(cm_correct_labels, curr_predictions)
 print('f1 score: {:.3f}, precision: {:.3f}, recall: {:.3f}'.format(score, precision, recall))
filename = this_run_file_prefix + 'submission_1.csv'
if best_fit_score < score:
best_fit_score = score
best_predictions = curr_predictions
choose_filename = filename
create_submission_file('./submission.csv', test_probabilities[1])
 create_submission_file(filename, test_probabilities[1])
 if no_models > 2:
curr_predictions = np.argmax(val_probabilities[2], axis = -1)
 score, precision, recall = getFitPrecisionRecall(cm_correct_labels, curr_predictions)
 print('f1 score: {:.3f}, precision: {:.3f}, recall: {:.3f}'.format(score, precision, recall))
filename = this_run_file_prefix + 'submission_2.csv'
if best_fit_score < score:
best_fit_score = score
best_predictions = curr_predictions
choose_filename = filename
create_submission_file('./submission.csv', test_probabilities[2])
 create_submission_file(filename, test_probabilities[2])
 if no_models > 1:
best_alpha0, best_alpha1, best_val_predictions, best_score, best_precision, best_recall = combine_two(cm_correct_labels, val_probabilities[0], val_probabilities[1])
 print('For indx', [0, 1], 'best_alpha0:', best_alpha0, 'best_alpha1:', best_alpha1, '.', datetime.now())
 print('f1 score: {:.3f}, precision: {:.3f}, recall: {:.3f}'.format(best_score, best_precision, best_recall))
combined_probabilities = best_alpha0 * test_probabilities[0] + best_alpha1 * test_probabilities[1]
filename = this_run_file_prefix + 'submission_01.csv'
if best_fit_score < best_score:
best_fit_score = best_score
best_predictions = best_val_predictions
choose_filename = filename
create_submission_file('./submission.csv', combined_probabilities)
 create_submission_file(filename, combined_probabilities)
 if no_models > 2:
best_alpha0, best_alpha1, best_val_predictions, best_score, best_precision, best_recall = combine_two(cm_correct_labels, val_probabilities[0], val_probabilities[2])
 print('For indx', [0, 2], 'best_alpha0:', best_alpha0, 'best_alpha1:', best_alpha1, '.', datetime.now())
 print('f1 score: {:.3f}, precision: {:.3f}, recall: {:.3f}'.format(best_score, best_precision, best_recall))
combined_probabilities = best_alpha0 * test_probabilities[0] + best_alpha1 * test_probabilities[2]
filename = this_run_file_prefix + 'submission_02.csv'
if best_fit_score < best_score:
best_fit_score = best_score
best_predictions = best_val_predictions
choose_filename = filename
create_submission_file('./submission.csv', combined_probabilities)
 create_submission_file(filename, combined_probabilities)
 best_alpha0, best_alpha1, best_val_predictions, best_score, best_precision, best_recall = combine_two(cm_correct_labels, val_probabilities[1], val_probabilities[2])
 print('For indx', [1, 2], 'best_alpha0:', best_alpha0, 'best_alpha1:', best_alpha1, '.', datetime.now())
 print('f1 score: {:.3f}, precision: {:.3f}, recall: {:.3f}'.format(best_score, best_precision, best_recall))
combined_probabilities = best_alpha0 * test_probabilities[1] + best_alpha1 * test_probabilities[2]
filename = this_run_file_prefix + 'submission_12.csv'
if best_fit_score < best_score:
best_fit_score = best_score
best_predictions = best_val_predictions
choose_filename = filename
create_submission_file('./submission.csv', combined_probabilities)
 create_submission_file(filename, combined_probabilities)
 best_alpha0, best_alpha1, best_alpha2, best_val_predictions, best_score, best_precision, best_recall = combine_three(cm_correct_labels, val_probabilities[0], val_probabilities[1], val_probabilities[2])
 print('For indx', [0, 1, 2], 'best_alpha0:', best_alpha0, 'best_alpha1:', best_alpha1, 'best_alpha2:', best_alpha2, '.', datetime.now())
 print('f1 score: {:.3f}, precision: {:.3f}, recall: {:.3f}'.format(best_score, best_precision, best_recall))
combined_probabilities = best_alpha0 * test_probabilities[0] + best_alpha1 * test_probabilities[1] + best_alpha2 * test_probabilities[2]
filename = this_run_file_prefix + 'submission_012.csv'
if best_fit_score < best_score:
best_fit_score = best_score
best_predictions = best_val_predictions
choose_filename = filename
create_submission_file('./submission.csv', combined_probabilities)
 create_submission_file(filename, combined_probabilities)
 cmat = confusion_matrix(cm_correct_labels, best_predictions, labels = range(len(CLASSES)))
 cmat =(cmat.T / cmat.sum(axis = -1)).T
display_confusion_matrix(cmat, score, precision, recall)
 print('Best score from all combination was', best_fit_score, '.For submission file used is', choose_filename)
 return best_fit_score, best_predictions
y_train = np.log(y_train )<drop_column>
best_predictions = cm_predictions
run_this = False
if no_of_models > 1 and run_this:
bp = get_best_combination(no_of_models, cm_correct_labels_results[0], val_probabilities, test_probabilities)
 best_predictions = bp
multicol_features = ['GarageCars', 'PoolQC', 'FireplaceQu', 'GarageCond']
X = X.drop(multicol_features, axis = 1)
 X_test = X_test.drop(multicol_features, axis = 1 )<normalization>
use_correct_labels = cm_correct_labels_results[0]
use_val_predictions = best_predictions
<train_on_grid><EOS> 
correct_labels_cnt = 0
incorrect_labels_cnt = 0
correct_labels = []
incorrect_labels = []
vals_actual_true = {}
vals_tp = {}
vals_fn = {}
vals_fp = {}
for i in range(len(CLASSES)) :
vals_actual_true[i] = 0
vals_tp[i] = 0
vals_fn[i] = 0
vals_fp[i] = 0
for i in range(len(use_correct_labels)) :
correct_label = use_correct_labels[i]
predict_label = use_val_predictions[i]
vals_actual_true[correct_label] = vals_actual_true[correct_label] + 1
if use_val_predictions[i] != use_correct_labels[i]:
incorrect_labels_cnt = incorrect_labels_cnt + 1
incorrect_labels.append(i)
 vals_fn[correct_label] = vals_fn[correct_label] + 1
vals_fp[predict_label] = vals_fp[predict_label] + 1
else:
correct_labels_cnt = correct_labels_cnt + 1
correct_labels.append(i)
 vals_tp[correct_label] = vals_tp[correct_label] + 1
print('Number of correct_labels is {}, incorrect_labels is {}'.format(correct_labels_cnt, incorrect_labels_cnt))
print('Incorrect labels', incorrect_labels)