class MachineLearning:
def __init__(self, worker):
self.worker = worker
self.ldb = LotteryDatabase()
# variables
self.x = None
self.y = None
self.x_train = None
self.x_validation = None
self.y_train = None
self.y_validation = None
self.N_FOLDS = 5
self.MAX_EVALS = 20
self.RANDOM_STATE = 42
self.training_size = 15
self.n_increment = 10
self.curr_game = CONFIG['games']['mini_lotto']
# features
self.table_headers = []
self.table_name = self.worker.table_name
self.features = self.curr_game['features']
for i in range(self.worker.window.list_model.count()):
feature_len = self.features[self.worker.window.list_model.item(
i).text()]['length'] + 1
feature_header = self.features[self.worker.window.list_model.item(
i).text()]['header']
self.table_headers += [
feature_header + str(n) + ' INTEGER'
for n in range(1, feature_len)
]
def generate_df_pieces(self, connection, chunk_size, offset, ids):
last_row = self.ldb.get_table_length(self.table_name)
chunks = int(math.ceil(last_row / chunk_size))
n_chunk = 1
self.ldb.delete_view('tempView')
self.ldb.create_view(
'tempView',
",".join(['DRAFT_ID'] + self.table_headers + ['LABEL']),
self.table_name)
while True:
self.worker.signal_status.emit(
str.format('Collecting data from database... {} of {}',
n_chunk, chunks))
sql_ct = "SELECT * FROM tempView WHERE DRAFT_ID <= %d limit %d offset %d" % (
ids, chunk_size, offset)
df_piece = pd.read_sql_query(sql_ct, connection)
if not df_piece.shape[0]:
break
yield df_piece
if df_piece.shape[0] < chunk_size:
break
offset += chunk_size
n_chunk += 1
self.worker.signal_status.emit('')
def embedded_learning(self, input_array, limit=0, draft_id=0):
original_len = self.ldb.get_table_length('INPUT_' +
self.curr_game['database'])
dataset = pd.concat(
self.generate_df_pieces(self.ldb.conn, 100000, 0,
original_len - limit))
array = dataset.values
self.x = array[:, 1:len(self.table_headers) + 1]
self.y = array[:, len(self.table_headers) + 1]
tb._SYMBOLIC_SCOPE.value = True
model = self.choose_model(keras=True)
self.worker.table_name = 'PREDICT_' + self.worker.window.combo_predict_model.currentText(
)
convert = ConvertModel(self.worker,
list(map(int, input_array.split(" "))), limit)
convert.create_prediction_model(input_array)
self.table_name = 'PREDICT_' + self.worker.window.combo_predict_model.currentText(
)
output_dataset = pd.concat(
self.generate_df_pieces(self.ldb.conn, 10000, 0, 0))
output_array = output_dataset.values
output_x = output_array[:, 1:len(self.table_headers) + 1]
original_len = self.ldb.get_table_length(self.worker.table_name) + 1
now = datetime.datetime.now()
file_name_r = str.format(
'{} {}', self.worker.window.combo_predict_model.currentText(),
now.strftime("%Y-%m-%d %H %M %S")) + model['info']
export_columns = ['DRAFT_NR', 'FIRST', 'SECOND', 'THIRD', 'FOURTH', 'FIFTH', 'ODD_EVEN', 'LOW_HIGH', 'LA_JOLLA',
'SCORE_ALL', 'SCORE_TOP', 'SCORE_LESS', 'SCORE_2', 'SCORE_3', 'LABEL'] + \
['OUTPUT ' + str(n) for n in range(1, self.training_size+1)]
with open('archived/' + file_name_r + '.csv', 'a',
newline='') as csv_file:
writer = csv.writer(csv_file)
writer.writerow(export_columns)
score_all, score_2, score_3, score_top, score_less = 0, 0, 0, 0, 0
pairs_two = convert.get_latest_pairs(2)
pairs_three = convert.get_latest_pairs(3)
latest_numbers = convert.get_latest_top()
top_numbers = nlargest(20, latest_numbers, key=latest_numbers.get)
less_numbers = nsmallest(20,
latest_numbers,
key=latest_numbers.get)
head = ','.join(['LA_JOLLA_' + str(n) for n in range(1, 6)])
# self.ldb.db_delete_view('LA_JOLLA_VIEW')
# self.ldb.db_create_view('LA_JOLLA_VIEW', head, 'LA JOLLA')
# self.ldb.db_execute('SELECT * FROM LA_JOLLA_VIEW')
la_jolla_db = self.ldb.c.fetchall()
for o in range(1, original_len):
fetch_one = list(self.ldb.fetchone(self.table_name, o))
originals = fetch_one[2:self.curr_game['length'] + 2]
label_column = [fetch_one[-1]]
output_list = [
n + 1 for n in range(0, len(originals))
if originals[n] == 1
]
odd_count = len(
list(filter(lambda x: (x % 2 != 0), output_list)))
even_count = len(
list(filter(lambda x: (x % 2 == 0), output_list)))
if even_count > odd_count:
odd_even_check = 1
else:
odd_even_check = 0
high_low = sum(x > 21 for x in output_list)
decrease = 0
for top in top_numbers:
if int(top) in output_list:
score_all += (1 - decrease)
score_top += (1 - decrease)
decrease += 0.05
decrease = 0
for top in less_numbers:
if int(top) in output_list:
# score_all += (1 - decrease)
score_less += (1 - decrease)
decrease += 0.05
output_counter = Counter(combinations(output_list, 2))
decrease = 0
for pair in pairs_two:
if pair in output_counter:
score_all += (1 - decrease)
score_2 += (1 - decrease)
decrease += 0.01
output_counter = Counter(combinations(output_list, 3))
decrease = 0
for pair in pairs_three:
if pair in output_counter:
score_all += (1 - decrease)
score_3 += (1 - decrease)
decrease += 0.01
if output_list in la_jolla_db:
la_jolla = 1
else:
la_jolla = 0
output_list = [draft_id] + output_list + [odd_even_check] + [high_low] + [la_jolla] +\
[score_all] + [score_top] + [score_less] + [score_2] + [score_3] + label_column
writer.writerow(output_list)
score_all, score_2, score_3, score_top, score_less = 0, 0, 0, 0, 0
self.worker.signal_status.emit('')
if self.worker.window.check_keras.isChecked():
model['model'].fit(self.x,
self.y,
nb_epoch=100,
batch_size=212,
verbose=2)
prediction = model['model'].predict(output_x)
combined_set = list(map(str, prediction))
with open('archived/' + file_name_r + '.csv',
'r') as read_csv_file:
csv_input = csv.reader(read_csv_file)
next(csv_input)
now = datetime.datetime.now()
file_name_w = str.format(
'{} {}',
self.worker.window.combo_predict_model.currentText(),
now.strftime("%Y-%m-%d %H %M %S")) + model['info']
with open('archived/' + file_name_w + '.csv', 'w',
newline='') as csv_file:
writer = csv.writer(csv_file)
writer.writerow(export_columns)
for row, o in zip(csv_input, combined_set):
writer.writerow(row + [o])
os.remove('archived/' + file_name_r + '.csv')
file_name_r = file_name_w
elif self.worker.window.combo_predict_ml.currentText() == 'LogisticRegression' or \
self.worker.window.combo_predict_ml.currentText() == 'SGDClassifier':
model['model'].fit(self.x, self.y)
prediction = model['model'].predict(output_x)
combined_set = list(map(str, prediction))
with open('archived/' + file_name_r + '.csv',
'r') as read_csv_file:
csv_input = csv.reader(read_csv_file)
next(csv_input)
now = datetime.datetime.now()
file_name_w = str.format(
'{} {}',
self.worker.window.combo_predict_model.currentText(),
now.strftime("%Y-%m-%d %H %M %S")) + model['info']
with open('archived/' + file_name_w + '.csv', 'w',
newline='') as csv_file:
writer = csv.writer(csv_file)
writer.writerow(export_columns)
for row, o in zip(csv_input, combined_set):
writer.writerow(row + [o])
os.remove('archived/' + file_name_r + '.csv')
file_name_r = file_name_w
else:
for t in range(self.training_size):
self.worker.signal_status.emit(
str.format('Training in process... {} of {}', t + 1,
self.training_size))
model['model'].n_estimators += self.n_increment
model['model'].fit(self.x, self.y)
prediction = model['model'].predict(output_x)
combined_set = list(map(str, prediction))
with open('archived/' + file_name_r + '.csv',
'r') as read_csv_file:
csv_input = csv.reader(read_csv_file)
next(csv_input)
now = datetime.datetime.now()
file_name_w = str.format(
'{} {}',
self.worker.window.combo_predict_model.currentText(),
now.strftime("%Y-%m-%d %H %M %S")) + model['info']
with open('archived/' + file_name_w + '.csv',
'w',
newline='') as csv_file:
writer = csv.writer(csv_file)
writer.writerow(export_columns)
for row, o in zip(csv_input, combined_set):
writer.writerow(row + [o])
os.remove('archived/' + file_name_r + '.csv')
file_name_r = file_name_w
self.worker.signal_status.emit('')
msg = ''
# msg = "Algorithm: RandomForestClassifier" + '\n' + \
# "Number of estimators: {}".format(forest.n_estimators) + '\n' + \
# "Accuracy on training set: {:.3f}".format(forest.score(x_train, y_train)) + '\n' + \
# "Accuracy on test set: {:.3f}".format(forest.score(x_validation, y_validation))
return msg
def choose_model(self, params=None, fresh=False, keras=False):
model, info = None, None
if keras:
model = Sequential()
model.add(
Dense(output_dim=220,
kernel_initializer='uniform',
input_dim=int(self.x.shape[1])))
model.add(Activation(activation='relu'))
model.add(Dropout(0.27208339620963506))
model.add(
Dense(output_dim=205, kernel_initializer="glorot_uniform"))
model.add(Activation(activation='relu'))
model.add(Dropout(0.29152160619480066))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='rmsprop')
elif self.worker.window.list_ml.currentItem().text(
) == 'RandomForestClassifier':
if params is not None:
model = RandomForestClassifier(**params)
elif fresh:
model = RandomForestClassifier()
else:
model = RandomForestClassifier(warm_start=True,
n_estimators=1,
n_jobs=-1,
random_state=self.RANDOM_STATE)
# model = RandomForestClassifier(warm_start=True, n_estimators=1, n_jobs=-1, random_state=self.RANDOM_STATE)
info = ' RFC ' + self.worker.window.combo_db.currentText()
elif self.worker.window.list_ml.currentItem().text(
) == 'RandomForestRegressor':
if params is not None:
model = RandomForestRegressor(**params)
elif fresh:
model = RandomForestRegressor()
else:
model = RandomForestRegressor(warm_start=True,
n_estimators=1,
n_jobs=-1,
random_state=self.RANDOM_STATE)
info = ' RFR ' + self.worker.window.combo_db.currentText()
elif self.worker.window.list_ml.currentItem().text(
) == 'LogisticRegression':
if params is not None:
model = linear_model.LogisticRegression(**params)
elif fresh:
model = linear_model.LogisticRegression()
else:
model = linear_model.LogisticRegression(C=50,
solver='liblinear')
info = ' LR ' + self.worker.window.combo_db.currentText()
elif self.worker.window.list_ml.currentItem().text(
) == 'SGDClassifier':
if params is not None:
model = linear_model.SGDClassifier(**params)
elif fresh:
model = linear_model.SGDClassifier()
else:
model = linear_model.SGDClassifier(class_weight='balanced',
loss='hinge',
max_iter=2426,
tol=1.6246894453989777e-05,
warm_start=True)
# model = linear_model.SGDClassifier(class_weight='balanced', loss='log', max_iter=2330, tol=7.289319599768096e-05)
# model = linear_model.SGDClassifier(max_iter=1486, tol=4.663673194605843e-05, loss='log', fit_intercept=False)
# model = linear_model.SGDClassifier(max_iter=840, tol=15.8197115265907305e-05, class_weight='balanced', loss='modified_huber')
info = ' SGD ' + self.worker.window.combo_db.currentText()
return {'model': model, 'info': info}
def choose_space(self, keras=False):
space = {}
if keras:
space = {
'choice':
hp.choice('num_layers',
[{
'layers': 'two',
}, {
'layers': 'three',
'units3': hp.uniform('units3', 64, 1024),
'dropout3': hp.uniform('dropout3', .25, .75)
}]),
'units1':
hp.uniform('units1', 64, 1024),
'units2':
hp.uniform('units2', 64, 1024),
'dropout1':
hp.uniform('dropout1', .25, .75),
'dropout2':
hp.uniform('dropout2', .25, .75),
'batch_size':
hp.uniform('batch_size', 28, 128),
'nb_epochs':
100,
'optimizer':
hp.choice('optimizer', ['adadelta', 'adam', 'rmsprop']),
'activation':
'relu'
}
elif self.worker.window.list_ml.currentItem().text(
) == 'RandomForestClassifier':
space = {
'n_estimators':
hp.choice('n_estimators', range(100, 1500)),
'class_weight':
hp.choice('class_weight',
['balanced', 'balanced_subsample', None]),
'max_features':
hp.choice('max_features', ['auto', 'sqrt', 'log2']),
'bootstrap':
hp.choice('bootstrap', [True, False]),
'max_depth':
hp.choice('max_depth', [None, 1, 3]),
'criterion':
hp.choice('criterion', ['gini', 'entropy'])
}
elif self.worker.window.list_ml.currentItem().text(
) == 'RandomForestRegressor':
space = {
'n_estimators':
hp.choice('n_estimators', range(10, 150)),
'warm_start':
hp.choice('warm_start', [True, False]),
'class_weight':
hp.choice('class_weight',
['balanced', 'balanced_subsample', None]),
'max_features':
hp.choice('max_features', ['auto', 'sqrt']),
'bootstrap':
hp.choice('bootstrap', [True, False]),
'max_depth':
hp.choice('max_depth', [None, 1, 2, 3]),
'min_samples_split':
hp.choice('min_samples_split', [2, 3]),
'min_samples_leaf':
hp.choice('min_samples_leaf', [1, 2])
}
elif self.worker.window.list_ml.currentItem().text(
) == 'LogisticRegression':
space = {
'solver': hp.choice('solver', ['newton-cg', 'lbfgs', 'sag']),
'warm_start': hp.choice('warm_start', [True, False]),
'class_weight': hp.choice('class_weight', ['balanced', None]),
'tol': hp.uniform('tol', 0.00001, 0.0001),
'C': hp.uniform('C', 1.0, 50.0),
'fit_intercept': hp.choice('fit_intercept', [True, False]),
'max_iter': hp.choice('max_iter', range(100, 3000))
}
elif self.worker.window.list_ml.currentItem().text(
) == 'SGDClassifier':
space = {
'class_weight':
hp.choice('class_weight', [None, 'balanced']),
'warm_start':
hp.choice('warm_start', [True, False]),
'fit_intercept':
hp.choice('fit_intercept', [True, False]),
'tol':
hp.uniform('tol', 0.00001, 0.0001),
'loss':
hp.choice('loss',
['hinge', 'log', 'squared_hinge', 'modified_huber']),
'max_iter':
hp.choice('max_iter', range(500, 3000))
}
return space
def sklearn_model_train(self):
print(self.worker.window.list_ml.currentItem().text())
dataset = pd.concat(
self.generate_df_pieces(self.ldb.conn, 100000, offset=0, ids=5000))
array = dataset.values
self.x = array[:, :len(self.table_headers)]
self.y = array[:, len(self.table_headers)]
x_train, x_validation, y_train, y_validation = model_selection.train_test_split(
self.x, self.y, test_size=0.2, random_state=self.RANDOM_STATE)
space = self.choose_space()
bayes_trials = Trials()
best = fmin(fn=self.sklearn_objective,
space=space,
algo=tpe.suggest,
max_evals=self.MAX_EVALS,
trials=bayes_trials)
print(best)
for bt in bayes_trials:
print(bt['result']['loss'])
print(bt['result']['params'])
model = self.choose_model(space_eval(space, best))
model['model'].fit(x_train, y_train)
y_pred = model['model'].predict(x_validation)
msg = 'Accuracy Score : ' + str(accuracy_score(y_validation, y_pred)) + '\n' + \
'Precision Score : ' + str(precision_score(y_validation, y_pred)) + '\n' + \
'Recall Score : ' + str(recall_score(y_validation, y_pred)) + '\n' + \
'F1 Score : ' + str(f1_score(y_validation, y_pred)) + '\n' + \
'ROC_AUC Score:' + str(roc_auc_score(y_validation, y_pred))
# 'Confusion Matrix : \n' + str(confusion_matrix(y_validation, y_pred))
plt.figure()
self.plot_confusion_matrix(confusion_matrix(y_validation, y_pred),
classes=[1, 0])
self.worker.signal_status.emit('')
self.worker.signal_infobox.emit("Completed", msg)
plt.show()
def sklearn_objective(self, params, n_folds=5):
clf = self.choose_model(fresh=True)
rus = RandomUnderSampler()
# pipeline = make_pipeline(rus, clf)
scores = model_selection.cross_val_score(clf['model'],
self.x,
self.y,
cv=n_folds,
scoring='f1_macro')
best_score = max(scores)
loss = 1 - best_score
return {'loss': loss, 'params': params, 'status': STATUS_OK}
def keras_model_train(self):
dataset = pd.concat(
self.generate_df_pieces(self.ldb.conn, 100000, offset=0, ids=5000))
array = dataset.values
self.x = array[:, :len(self.table_headers)]
self.y = array[:, len(self.table_headers)]
self.x_train, self.x_validation, self.y_train, self.y_validation = model_selection.train_test_split(
self.x, self.y, test_size=0.2, random_state=self.RANDOM_STATE)
space = self.choose_space(keras=True)
tb._SYMBOLIC_SCOPE.value = True
bayes_trials = Trials()
best = fmin(fn=self.keras_objective,
space=space,
algo=tpe.suggest,
max_evals=self.MAX_EVALS,
trials=bayes_trials)
print(best)
for bt in bayes_trials:
print(bt['result']['loss'])
print(bt['result']['params'])
def keras_objective(self, params):
model = Sequential()
model.add(
Dense(output_dim=params['units1'],
input_dim=self.x_train.shape[1]))
model.add(Activation(params['activation']))
model.add(Dropout(params['dropout1']))
model.add(Dense(output_dim=params['units2'], init="glorot_uniform"))
model.add(Activation(params['activation']))
model.add(Dropout(params['dropout2']))
if params['choice']['layers'] == 'three':
model.add(
Dense(output_dim=params['choice']['units3'],
init="glorot_uniform"))
model.add(Activation(params['activation']))
model.add(Dropout(params['choice']['dropout3']))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer=params['optimizer'])
model.fit(self.x_train,
self.y_train,
nb_epoch=params['nb_epochs'],
batch_size=params['batch_size'],
verbose=0)
pred_auc = model.predict_proba(self.x_validation,
batch_size=128,
verbose=0)
acc = roc_auc_score(self.y_validation, pred_auc)
# print('AUC:', acc)
# sys.stdout.flush()
return {'loss': -acc, 'params': params}
@staticmethod
def plot_confusion_matrix(cm, classes, normalize=False, cmap=plt.cm.Blues):
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title('Confusion matrix')
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.
for i, j in product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j,
i,
format(cm[i, j], fmt),
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.tight_layout()
def validate_estimators(self, x, y):
x_train, x_validation, y_train, y_validation = model_selection.train_test_split(
x, y, test_size=0.3, random_state=0)
n_estimators = []
train_results = []
test_results = []
rf = RandomForestRegressor(warm_start=True, n_estimators=0, n_jobs=-1)
# rf = RandomForestClassifier(warm_start=True, n_estimators=0, n_jobs=-1)
for t in range(self.training_size):
rf.n_estimators += 3
# rf.n_iter += 2
# n_estimators += [rf.n_iter]
n_estimators += [rf.n_estimators]
self.worker.signal_status.emit(
'Validating estimators: {} of {}. Current estimator: {}'.
format(t + 1, self.training_size, rf.n_estimators))
rf.fit(x_train, y_train)
train_pred = rf.predict(x_train)
false_positive_rate, true_positive_rate, thresholds = roc_curve(
y_train, train_pred)
roc_auc = auc(false_positive_rate, true_positive_rate)
train_results.append(roc_auc)
y_pred = rf.predict(x_validation)
false_positive_rate, true_positive_rate, thresholds = roc_curve(
y_validation, y_pred)
roc_auc = auc(false_positive_rate, true_positive_rate)
test_results.append(roc_auc)
line1, = plt.plot(n_estimators, train_results, 'b', label="Train AUC")
line2, = plt.plot(n_estimators, test_results, 'r', label="Test AUC")
plt.legend(handler_map={line1: HandlerLine2D(numpoints=2)})
plt.ylabel('AUC score')
plt.xlabel('n_estimators')
plt.show()
def validate_max_depth(self, x, y):
x_train, x_validation, y_train, y_validation = model_selection.train_test_split(
x, y, test_size=0.3, random_state=0)
max_depths = np.linspace(1, 32, 32, endpoint=True)
train_results = []
test_results = []
for max_depth in max_depths:
rf = RandomForestClassifier(warm_start=True,
n_estimators=10,
max_depth=max_depth,
n_jobs=-1)
self.worker.signal_status.emit(
'Validating max depth: {} of {}.'.format(
max_depth, len(max_depths)))
rf.fit(x_train, y_train)
train_pred = rf.predict(x_train)
false_positive_rate, true_positive_rate, thresholds = roc_curve(
y_train, train_pred)
roc_auc = auc(false_positive_rate, true_positive_rate)
train_results.append(roc_auc)
y_pred = rf.predict(x_validation)
false_positive_rate, true_positive_rate, thresholds = roc_curve(
y_validation, y_pred)
roc_auc = auc(false_positive_rate, true_positive_rate)
test_results.append(roc_auc)
line1, = plt.plot(max_depths, train_results, 'b', label="Train AUC")
line2, = plt.plot(max_depths, test_results, 'r', label="Test AUC")
plt.legend(handler_map={line1: HandlerLine2D(numpoints=2)})
plt.ylabel('AUC score')
plt.xlabel('Tree depth')
plt.show()
def validate_min_sample_split(self, x, y):
x_train, x_validation, y_train, y_validation = model_selection.train_test_split(
x, y, test_size=0.3, random_state=0)
min_samples_splits = [0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0]
train_results = []
test_results = []
for min_samples_split in min_samples_splits:
rf = RandomForestClassifier(warm_start=True,
n_estimators=10,
min_samples_split=min_samples_split,
n_jobs=-1)
self.worker.signal_status.emit(
'Validating min sample split: {} of {}.'.format(
min_samples_split, len(min_samples_splits)))
rf.fit(x_train, y_train)
train_pred = rf.predict(x_train)
false_positive_rate, true_positive_rate, thresholds = roc_curve(
y_train, train_pred)
roc_auc = auc(false_positive_rate, true_positive_rate)
train_results.append(roc_auc)
y_pred = rf.predict(x_validation)
false_positive_rate, true_positive_rate, thresholds = roc_curve(
y_validation, y_pred)
roc_auc = auc(false_positive_rate, true_positive_rate)
test_results.append(roc_auc)
line1, = plt.plot(min_samples_splits,
train_results,
'b',
label="Train AUC")
line2, = plt.plot(min_samples_splits,
test_results,
'r',
label="Test AUC")
plt.legend(handler_map={line1: HandlerLine2D(numpoints=2)})
plt.ylabel('AUC score')
plt.xlabel('min samples split')
plt.show()
def validate_min_sample_leaf(self, x, y):
x_train, x_validation, y_train, y_validation = model_selection.train_test_split(
x, y, test_size=0.3, random_state=0)
min_samples_leafs = [1, 2, 3, 4, 5]
train_results = []
test_results = []
for min_samples_leaf in min_samples_leafs:
rf = RandomForestClassifier(warm_start=True,
n_estimators=10,
min_samples_leaf=min_samples_leaf,
n_jobs=-1)
self.worker.signal_status.emit(
'Validating min sample leaf: {} of {}.'.format(
min_samples_leaf, len(min_samples_leafs)))
rf.fit(x_train, y_train)
train_pred = rf.predict(x_train)
false_positive_rate, true_positive_rate, thresholds = roc_curve(
y_train, train_pred)
roc_auc = auc(false_positive_rate, true_positive_rate)
train_results.append(roc_auc)
y_pred = rf.predict(x_validation)
false_positive_rate, true_positive_rate, thresholds = roc_curve(
y_validation, y_pred)
roc_auc = auc(false_positive_rate, true_positive_rate)
test_results.append(roc_auc)
line1, = plt.plot(min_samples_leafs,
train_results,
'b',
label="Train AUC")
line2, = plt.plot(min_samples_leafs,
test_results,
'r',
label="Test AUC")
plt.legend(handler_map={line1: HandlerLine2D(numpoints=2)})
plt.ylabel('AUC score')
plt.xlabel('min samples leaf')
plt.show()
def validate_max_features(self):
pass
def random_forest_predict(self):
output_headers = ",".join(['ID INTEGER PRIMARY KEY'] +
['OUTPUT_LABEL INTEGER'])
self.ldb.delete_table('OUTPUT_prediction')
self.ldb.create_table('OUTPUT_prediction', output_headers)
ids = 1
dataset = pd.concat(
self.generate_df_pieces(self.ldb.conn, 1000, offset=0))
array = dataset.values
x = array[:, :len(self.table_headers)]
filename = 'random_forest.sav'
class TestTF:
def __init__(self):
self.ldb = LotteryDatabase(CONFIG['database'])
self.x = None
self.y = None
self.x_train = None
self.x_validation = None
self.y_train = None
self.y_validation = None
self.curr_game = CONFIG['games']['mini_lotto']
self.table_headers = []
self.features = self.curr_game['features']
feat = ['number_map', 'number_cycles', 'cool numbers']
for i in feat:
feature_len = self.features[i]['length'] + 1
feature_header = self.features[i]['header']
self.table_headers += [
feature_header + str(n) + ' INTEGER'
for n in range(1, feature_len)
]
def main_tf(self):
# dataset = dataframe.read_sql_table(table='MODEL_ml', uri='sqlite:///' + config['database'], index_col='ID', npartitions=6)
dataset = pd.concat(
self.generate_df_pieces(self.ldb.conn, 100000, offset=0, ids=5000))
# dataset.compute()
array = dataset.values
self.x = array[:, :len(self.table_headers)]
self.y = array[:, len(self.table_headers)]
self.x_train, self.x_validation, self.y_train, self.y_validation = model_selection.train_test_split(
self.x, self.y, test_size=0.2, random_state=42)
bayes_trials = Trials()
best = fmin(fn=self.keras_objective,
space=space,
algo=tpe.suggest,
max_evals=10,
trials=bayes_trials)
print(best)
for bt in bayes_trials:
print(bt['result']['loss'])
print(bt['result']['params'])
def generate_df_pieces(self, connection, chunk_size, offset, ids):
last_row = self.ldb.get_table_length('MODEL_ml')
chunks = int(math.ceil(last_row / chunk_size))
n_chunk = 1
self.ldb.delete_view('tempView')
self.ldb.create_view(
'tempView',
",".join(['DRAFT_ID'] + self.table_headers + ['LABEL']),
'MODEL_ml')
while True:
print(
str.format('Collecting data from database... {} of {}',
n_chunk, chunks))
sql_ct = "SELECT * FROM tempView WHERE DRAFT_ID <= %d limit %d offset %d" % (
ids, chunk_size, offset)
df_piece = pd.read_sql_query(sql_ct, connection)
if not df_piece.shape[0]:
break
yield df_piece
if df_piece.shape[0] < chunk_size:
break
offset += chunk_size
n_chunk += 1
def keras_objective(self, params):
model = Sequential()
model.add(
Dense(output_dim=params['units1'],
kernel_initializer='uniform',
input_dim=int(self.x_train.shape[1])))
model.add(Activation(params['activation']))
model.add(Dropout(params['dropout1']))
model.add(
Dense(output_dim=params['units2'],
kernel_initializer="glorot_uniform"))
model.add(Activation(params['activation']))
model.add(Dropout(params['dropout2']))
if params['choice']['layers'] == 'three':
model.add(
Dense(output_dim=params['choice']['units3'],
kernel_initializer="glorot_uniform"))
model.add(Activation(params['activation']))
model.add(Dropout(params['choice']['dropout3']))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer=params['optimizer'])
model.fit(self.x_train,
self.y_train,
nb_epoch=params['epochs'],
batch_size=params['batch_size'],
verbose=2)
pred_auc = model.predict_proba(self.x_validation,
batch_size=params['batch_size'],
verbose=2)
acc = roc_auc_score(self.y_validation, pred_auc)
print('AUC:', acc)
sys.stdout.flush()
return {'loss': -acc, 'params': params, 'status': STATUS_OK}